Interferometric scattering microscopy

ABSTRACT

An interferometric scattering microscope is adapted by performing spatial filtering of output light, which comprises both light scattered from a sample location and illuminating light reflected from the sample location, prior to detection of the output light. The spatial filtering passes the reflected illumination light but with a reduction in intensity that is greater within a predetermined numerical aperture than at larger numerical apertures. This enhances the imaging contrast for coherent illumination, particularly for objects that are weak scatterers.

The present invention relates to interferometric scattering microscopy(referred to herein as iSCAT).

iSCAT has materialized as a powerful approach to both single particletracking with unique spatiotemporal resolution and label-freesensitivity down to the single molecule level. iSCAT is disclosed, forexample in Kukura et al., “High-speed nanoscopic tracking of theposition and orientation of a single virus”, Nature Methods 20096:923-935, and in Ortega-Arroyo et al. “Interferometric scatteringmicroscopy (iSCAT): new frontiers in ultrafast and ultrasensitiveoptical microscopy”, Physical Chemistry Chemical Physics 201214:15625-15636. Despite considerable potential, widespread applicationof iSCAT has been limited by the requirement for custom-builtmicroscopes, unconventional cameras and complex sample illumination,limiting that capabilities of iSCAT for the robust and accuratedetection, imaging and characterisation of objects as small as singlemolecules.

According to a first aspect of the present invention, there is providedan interferometric scattering microscope comprising: a sample holder forholding a sample in a sample location; an illumination source arrangedto provide illuminating light; a detector; an optical system beingarranged to direct illuminating light onto the sample location and beingarranged to collect output light in reflection, the output lightcomprising both light scattered from the sample location andilluminating light reflected from the sample location, and direct theoutput light to the detector; and a spatial filter positioned to filterthe output light, the spatial filter being arranged to pass output lightbut with a reduction in intensity that is greater within a predeterminednumerical aperture than at larger numerical apertures.

Whereas the overall arrangement of the microscope may be similar to aconventional iSCAT microscope, there is additionally provided a spatialfilter, which affects the output light. Specifically, the spatial filterpasses the output light but with a reduction in intensity that isgreater within a predetermined numerical aperture than at largernumerical apertures. As a result, the spatial filter selectively reducesthe intensity of the illuminating light over scattered light, by takingadvantage of the mismatch between the numerical aperture of reflectedilluminating light and of light scattered from objects in a sample atthe sample location. Thus, the spatial filter takes advantage of thedifferent directionalities of these two sources of light. The reflectedilluminating light will typically have a relatively small numericalaperture, whereas sub-diffraction-sized objects near a surface of thesample scatter light preferentially into high numerical apertures.Therefore, the reduction in intensity by the spatial filter at lownumerical apertures predominantly affects the illuminating light and hasa minimal effect on the scattered light, thereby maximising the imagingcontrast.

This effect may be maximised by arranging the spatial filter so that thepredetermined numerical aperture is identical or similar to thenumerical aperture of the illuminating light reflected from the samplelocation.

The first aspect of the present invention relates to a microscope thatoperates in reflection. In that case, the illuminating light thatreaches the detector is reflected predominantly from a surface of thesample, typically an interface between the sample and the sample holder,thereby providing interference with objects in the sample close to thatsurface. This provides an image with high contrast. This effect differsfrom a microscope operating in transmission wherein the illuminatinglight that reaches the detector is transmitted through the depth of thesample.

Operation in reflection has several advantages, which together allowhigh performance detection and quantification of weak scatteringobjects. Firstly, a relatively small amount, typically only 0.5%, of theillumination light is reflected at the commonly employed glass-waterinterface, while a significantly higher amount, typically greater than90%, of light scattered by a nanoscopic object at the interface isscattered back towards the illumination direction. This intrinsicallyimproves the ratio between scattered and reflected light fields morethan 1000-fold compared to a transmission geometry resulting in a largerinterferometric contrast. As a result, three orders of magnitude lessphotons need to be detected in the shot noise limited case to achievethe same nominal signal-to-noise given a specific scatterer,illumination intensity and exposure time. Secondly, in reflection,detection is much less sensitive to large scatterers present in solutionas forward scattering and its interference with illumination light isnot detected leading to higher background rejection.

These factors enhance the image quality and thereby enable high contrastdetection of weakly scattering objects. Weak scattering objects reflectlittle of the illumination light.

These advantages apply particularly for imaging of objects that scatterlight so weakly that accurate and precise imaging with other techniquesis impossible. For example, the present invention is particularly suitedto objects having a mass of 5000 kDa or less.

Similarly, the present invention may be applied with advantage to asample comprising objects having a scattering cross section with respectto the illuminating light of 10⁻¹⁷ m² or less. Typically such objectsmay also have a scattering cross section with respect to theilluminating light of 10⁻²⁶ m² or more, i.e. within a range from 10⁻¹⁷m² from 10⁻²⁶ m². Examples of objects that may be studied includeproteins or small aggregates thereof.

In order to image objects that are very weak scatterers, the spatialfilter is arranged to pass output light with a reduction in intensitywithin the predetermined numerical aperture to 10⁻² of the incidentintensity or less. Typically, the spatial filter may be arranged to passoutput light with a reduction in intensity within the predeterminednumerical aperture to 10⁻⁴ of the incident intensity or more, forexample in the range from 10⁻² to 10⁻⁴ of the incident intensity. Thus,in order to detect these weakly scattering objects a particular aperturemay be used.

The illuminating light may be spatially and temporally coherent, forexample using a laser as the light source. Widefield illumination in amicroscope is commonly achieved by focussing a collimated laser beaminto the back focal plane of the imaging objective, implying that it canbe efficiently coupled in and out of the microscope while minimallyaffecting the overall imaging performance.

The microscope may be an existing commercial microscope, which isadapted by incorporating the spatial filter. Such adaptation can beperformed very cheaply and simply, in contrast to existing iSCATmicroscopes, which have complex and expensive optical and electronicsetups in order to provide the required sensitivity, for exampleincluding requirements for an optical table, or expensive and complexoptics, electronics and expert operation, which requirements aresignificantly reduced or avoided altogether by incorporating a spatialfilter into an existing commercial microscope. That allows the presentinvention to be implemented in an extremely cost-effective manner. Forexample, larger fields of view may be provided without complex scanningarrangements and the use of low cost imaging cameras is enabled withoutloss of imaging performance or sensitivity.

According to a second aspect of the present invention, there is provideda spatial filter for filtering output light of an interferometricscattering microscope, the spatial filter having a similar function tothat of the first aspect.

According to a third aspect of the present invention, there is provideda method of adapting an interferometric scattering microscope byperforming spatial filtering of output light, the spatial filteringbeing similar to that performed in the first aspect.

According to a fourth aspect of the present invention, there is provideda method of quantifying the mass of an object, wherein the mass of saidobject is quantified by interferometric light scattering, and whereinsaid mass is quantified with up to 5% mass error. According to a fifthaspect of the present invention, there is provided a method of measuringor quantifying a change in the mass of an object, wherein the mass ofsaid object is measured or quantified by interferometric lightscattering.

To allow better understanding, embodiments of the present invention willnow be described by way of non-limitative example with reference to theaccompanying drawings, in which:

FIG. 1 is a schematic diagram of an iSCAT microscope;

FIG. 2 is an image captured by the iSCAT microscope; and

FIGS. 3, 4, 5, and 6 are schematic diagrams of modified iSCATmicroscopes;

FIG. 7 is a plot of scattering contrast against sequence molecularweight for a series of proteins derived using the microscope shown inFIG. 6;

FIGS. 8 and 9 are mass histograms for avidin in the absence and presenceof biotin, respectively, derived using the microscope shown in FIG. 6;and

FIG. 10 is a mass histogram for bovine serum albumin derived using themicroscope shown in FIG. 6.

FIGS. 11A-D illustrate the concept of interferometric scattering massspectrometry (interferometric light scattering (iSCAMS)). (A) Schematicof the experimental approach relying on immobilization of individualmolecules near a refractive index interface. Oligomeric states arecoloured differently for clarity. (B) Differential interferometricscattering image of BSA. Scale bar: 0.5 μm. (C) Representative images ofmonomers, dimers, trimers and tetramers of BSA. Scale bar: 200 nm. (D)Scatter plot of single molecule binding events and their scatteringcontrasts for 12 nM BSA from 14 movies (lower). Corresponding histogram(N=12209) and zoom of the region for larger species (upper). Thereduction in landing rate results from a drop in BSA concentration withtime due to the large surface-to-volume ratio of our sample cell.

FIGS. 12A-D illustrate characterization of interferometric lightscattering (iSCAMS) accuracy, precision, and dependence on molecularshape and identity. (A) Contrast vs molecular mass including proteinsused for mass calibration (black), characterization of shape dependence(yellow), protein-ligand binding (green), lipid nanodisc composition(red) and glycosylation (blue). Mass error (upper panel) is given as apercentage of the sequence mass relative to the given linear fit. (B)Nanodisc mass-measurement for different lipid compositions and proteinbelts. Masses obtained by alternative methodologies for MSP1D1/DMPC aremarked and extrapolated to the other compositions. The horizontal barsindicate the expected mass range as a function of characterizationtechnique, with the thin bar indicating the contrast measured, and thethick bar representative of the measurement uncertainty in terms of thestandard error of the mean for repeated experiments. For each sample,the upper text denotes the membrane scaffold protein (MSP) used, and thelower the lipids in the nanodisc. (C) Recorded differential contrast forEnv expressed in the presence or absence of kifunensine, and associatedmass ranges expected for different glycosylation levels. (D)Mass-sensitive detection of ligand binding using the biotin-streptavidinsystem according to the sequence mass of streptavidin and the masses ofbiotin and two biotinylated peptides relative to the calibrationobtained from A. Abbreviations used are summarized in SupplementaryTable S8.

FIGS. 13A and B illustrate single molecule mass analysis ofheterogeneous protein assembly. (A) Mass distributions for Env in thepresence of 0.5-40 nM BanLec monomer. Inset: zoom alongside expectedpositions for multiples of bound BanLec tetramers. (B) Oligomericfractions colored according to A vs BanLec concentration includingpredictions (solid) using the given cooperative model.

FIGS. 14A-E illustrate mass-imaging of mesoscopic dynamics. (A)Schematic of and interferometric light scattering (e.g. iSCAMS) imagesfor α-synuclein (1 μM) aggregation on a negatively charged bilayermembrane. (B) Initial growth rate vs. α-synuclein concentrationalongside the best fit assuming first order kinetics (solid). Inset:Individual growth trajectories (grey) and average (black) for 21particles from A. (C) Schematic and interferometric light scattering(e.g. iSCAMS) images of actin polymerization. The arrow highlights agrowing filament. (D) Representative traces of actin filament tipposition (grey) and corresponding detected steps (black). (E) Step andmass histogram from 1523 steps and 33 filaments including a fit to aGaussian mixture model (black) and individual contributions (colored).Scale bars: 1 μm. In these experiments, background correction involvedremoval of the static background prior to acquisition, rather thancontinuous differential imaging.).

FIG. 15 shows one-dimensional distributions of refractive index (top,bottom) and specific volume (left, right) for all proteins in sixgenomes, as calculated from the amino acid sequences, and thetwo-dimensional distribution of both quantities (middle). The top,right, and middle panels show the combined data from all genomes. Theleft and bottom panels show the respective distributions for theseparate genomes, renormalized to have identical areas.

FIGS. 16A-D illustrate data analysis. (A) Raw camera images before andafter the landing event in B-D showing image contrast due to coversliproughness. (B) Illustration of the image averaging and differentialimaging approach. The asterisk marks a landing event. Individual imagesare averaged into two consecutive blocks (blue and red), which arenormalized and divided to provide differential contrast. The mid-pointis scanned in time, meaning that the signal from stochastic landingevents grows and fades, as indicated by the black arrow. Scale bars: 1μm. (C) Corresponding cross-sections for the particle highlighted in B.(D) Corresponding signal magnitudes extracted by a fit to the PSF andfit (black).

FIGS. 17A-F show solution vs surface mass distributions. (A) Changes inbinding time distributions for BSA monomers and dimers from the samedata as shown in FIG. 1 and corresponding exponential fits. (B)Resulting binding rate constants for 11 different movies. (C) Bindingconstants for a variety of proteins exhibiting more than one oligomericstate studied in this work, normalized to the average binding constantfor each protein. (D) Plot of binding constant vs (molecularweight)^(−1/3), except for those exhibiting inverted behaviour butincluding protein samples exhibiting only a single oligomeric state aswell as those in C, together with a linear fit describing the behaviorexpected from diffusion scaling. (E) BSA mass distribution before (solidbars) and after (red line) scaling for mass-dependent diffusion. (F)Env-BanLec oligomeric evolution before (solid) and after (dashed)correction for surface effects.

FIGS. 18A-E show kernel densities for FIG. 11 (A) Calibration proteins:GroEL 14mer (802.6 kDa), thyroglobulin (669 kDa), non-muscle myosin 2b(597 kDa), HSP16.5 24mer (394.8 kDa), β-amylase (224.3 kDa) showing somedissociation at the low concentrations at which we measured, alcoholdehydrogenase (147.4 kDa), BSA (66.4 kDa), streptavidin (52.8 kDa). (B)Smooth-muscle myosin. (C) Biotin-streptavidin. (D) Lipid nanodiscs. (E)Env expressed in the presence and absence of kifunensine. The Kernelbandwidths were 3, 5, 5, 7.5, 10, 10, 12 and 15 kDa with increasing massfor A; 9 kDa for B; 3 kDa for C; 5 kDa for D; 10 kDa for E.

FIGS. 19A-C show noise, resolution and shape-dependencecharacterization. (A) Absolute and fractional mass residuals as afunction of molecular shape factor (48). (B) Standard deviation ofdifferential images as a function of integration time, for acquisitionat 1000 frames/s. The dashed line indicates the nominal noise floor, andthe solid line expectation based on shot noise. (C) Standard deviationof contrast histograms obtained for the 8 calibration proteins from FIG.11A, including a linear fit as a guide to the eye.

FIG. 20 shows EM images of SMM in the extended (6S, left) and folded(10S, middle) conformation. Cross-linking at 25 mM salt increased thefraction of SMM dimers (right). Scale bar: 50 nm.

FIG. 21 shows high-performance liquid chromatography of N-glycansreleased from Env expressed in the absence (top) and presence (bottom)of kifunensine. The corresponding average masses are determined to be1664 and 1885 Da, respectively, based on peak height.

FIGS. 22A-22B show initial growth rate distributions, and thioflavin-Tstaining for α-synuclein aggregation. (A) Growth rate histogramsunderpinning FIG. 4B from the main text. Total number of particlesanalyzed: 31, 20, 58, 46, 40, 30. (B) Fluorescence image afterthioflavin-T staining of a bilayer aggregation assay at 10 μM afterovernight incubation. Scale bar: 5 μm.

FIGS. 23A-H show addition of actin to individual filaments. (A)Representative 1.7×1.7 μm² images of short phalloidin-stabilized actinfilaments. (B) Scatter plot of detected steps at the two ends of thefilament for the filaments from A. (C) Macroscopic growth rate recordedfor 26, 14, 37 and 10 different filaments at increasing actinconcentration including a linear fit. (D) Step size histograms resultingfrom applying the step-finding algorithm to simulated step traces withdifferent step sizes. In each case, 16 filaments were simulated with atotal number of 1500 steps. The total number of detected steps were 22,301, 575, 704, 940, 1088 and 1247 for 0-8 nm steps. (E) Experimentalstep size distributions as a function of minimum delay time betweensteps including fits to a Gaussian mixture model. (F) Step sizesdetermined from E. (G) Representative images of the growing filament(left) and differential mass image (right) for 2.8, 5.6 and 8.4 nmsteps. The points indicate the centre of the differential mass and areoverlaid on the image of the filament tip. Scale bar: 200 nm. (H) Masscorresponding to 1, 2 and 3 subunit additions obtained from images suchas those shown in G, using 11, 14 and 8 events, respectively. The lineindicates a linear fit to 0, 1 and 2 subunit additions to obtain a stepsize-to-mass conversion.

In the microscopes and methods described herein, the light used may be:ultraviolet light (which may be defined herein as having wavelengths inthe range from 10 nm to 380 nm); visible light (which may be definedherein as having wavelengths in the range from 380 nm to 740 nm);infrared light (which may be defined herein as having wavelengths in therange from 740 nm to 300 μm). The light may be a mixture of wavelengths.Herein, the terms ‘optical’ and ‘optics’ are used to refer generally tothe light to which the methods are applied.

FIG. 1 illustrates an iSCAT microscope 1 which is arranged as follows.

The microscope 1 includes the following components that, except for thespatial filter described in more detail below, have a construction thatis conventional in the field of microscopy.

The microscope 1 comprises a sample holder 2 for holding a sample 3 at asample location. The sample 3 may be a liquid sample comprising objectsto be imaged, which are described in more detail below. The sampleholder 2 may take any form suitable for holding the sample 3. Typically,the sample holder 2 holds the sample 3 on a surface, which forms aninterface between the sample holder 2 and the sample 3. For example, thesample holder 2 may be a coverslip and/or may be made from glass. Thesample 3 may be provided on the sample holder 2 in a straightforwardmanner, for example using a micropipette.

The microscope 1 further comprises an illumination source 4 and adetector 5.

The illumination source 4 is arranged to provide illuminating light. Theilluminating light may be coherent light. For example, the illuminationsource 4 may be a laser. The wavelength of the illuminating light may beselected in dependence on the nature of the sample 3 and/or theproperties to be examined. In one example, the illuminating light has awavelength of 405 nm.

Optionally, the illumination light may be modulated spatially, to removespeckle patterns that arise from the coherent nature of the illuminationand laser noise, for example as detailed in Kukura et al., “High-speednanoscopic tracking of the position and orientation of a single virus”,Nature Methods 2009 6:923-935.

The detector 5 receives output light in reflection from the samplelocation. Typically, the microscope 1 may operate in a wide-field mode,in which case the detector 5 may be an image sensor that captures animage of the sample 3. The microscope 1 may alternatively operate in aconfocal mode, in which case the detector 5 may be an image sensor ormay be a point-like detector, such as a photo-diode, in which case ascanning arrangement may be used to scan a region of the sample 3 tobuild up an image. Examples of image sensors that may be employed as thedetector 5 include a CMOS (complementary metal-oxide semiconductor)image sensor or a CCD (charge-coupled device).

The microscope 1 further comprises an optical system 10 arranged betweenthe sample holder 2, the illumination source 4 and the detector 5. Theoptical system 10 is arranged as follows to direct illuminating lightonto the sample location for illuminating the sample 3, and to collectoutput light in reflection from the sample location and to direct theoutput light to the detector 5.

The optical system 10 includes an objective lens 11 which is a lenssystem disposed in front of the sample holder 2. The optical system 10also includes a condenser lens 12 and a tube lens 13.

The condenser lens 12 condenses illuminating light from the light source11 (shown by continuous lines in FIG. 1) through the objective lens 11onto the sample 3 at the sample location.

The objective lens 11 collects the output light which comprises both (a)illuminating light reflected from the sample location (shown bycontinuous lines in FIG. 1), and (b) light scattered from the sample 3at the sample location (shown by dotted lines in FIG. 1). The reflectedlight is predominantly reflected from the interface between the sampleholder 2 and the sample 3. Typically, this is a relatively weakreflection, for example a glass-water reflection. For example, theintensity of the reflected illuminating light may be of the order of0.5% of the intensity of the incident illuminating light. The scatteredlight is scattered by objects in the sample 3.

In a similar manner to conventional iSCAT, scattered light from objectsat or close to the surface of the sample constructively interfere withthe reflected light and so are visible in the image captured by thedetector 5. This effect differs from a microscope operating intransmission wherein the illuminating light that reaches the detector istransmitted through the depth of the sample leading to a much smallerimaging contrast.

As shown in FIG. 1, the reflected illuminating light and the scatteredlight have different directionalities. In particular, the reflectedilluminating light has a numerical aperture resulting from the geometryof the beam of light output by the light source 4 and the optical system6. The scattered light is scattered over a large range of angles and sofills larger numerical aperture than the reflected illuminating light.

The tube lens 13 focuses the output light from the objective lens 11onto the detector 5.

The optical system 6 also includes a beam splitter 14 that is arrangedto split the optical paths for the illuminating light from the lightsource 4 and the output light directed to the detector 5. Except for theprovision of a spatial filter as described below, the beam splitter 14may have a conventional construction that provides partial reflectionand partial transmission of light incident thereon. For example, thebeam splitter 14 may be a plate, typically provided with a film, whichmay be metallic or dielectric, arranged at 45° to the optical paths.Alternatively, the beam splitter 14 may be a cube beam splitter formedby a matched pair of prisms having a partially reflective film at theinterface between the prisms. Alternatively, the beam splitter 14 may bea polarising beam splitter, used in combination with a quarter waveplate between the beam splitter 14 and the sample 3.

In the example shown in FIG. 1, the light source 4 is offset from theoptical path of the objective lens 11 so that the illuminating lightfrom the light source 4 is reflected by the beam splitter 14 into theobjective lens 11, and conversely the detector 5 is aligned with theoptical path of the objective lens 11 so that the output light from thesample location is transmitted through the beam splitter 14 towards thedetector 5.

In addition to the components described above that may be of aconventional construction, the microscope 1 includes a spatial filter20. In the example shown in FIG. 1, the spatial filter 20 is formed onthe beam splitter 14 and is thereby positioned behind the back apertureof the objective lens 11, and so directly behind the back focal plane 15of the objective lens 11. Thus, the spatial filter 20 may be implementedwithout entering the objective lens as in phase contrast microscopy.Placing the spatial filter directly behind the entrance aperture of theobjective rather than in a conjugate plane (for example as describedbelow) has the distinct advantage of strongly suppressing backreflections originating from the numerous lenses within high numericalaperture microscope objectives. This, in turn, reduces imaging noise,lowers non-interferometric background and reduces the experimentalcomplexity, number of optics and optical pathlength leading to increasedstability of the optical setup and thus image quality.

However this location is not essential and a spatial filter having anequivalent function may be provided elsewhere as described below.

The spatial filter 20 is thereby positioned to filter the output lightpassing to the detector 5. In the example shown in FIG. 1 in which thedetector 5 is aligned with the optical path of the objective lens 11,the spatial filter 20 is therefore transmissive.

The spatial filter 20 is partially transmissive and therefore passes theoutput light, which includes the reflected illumination light, but witha reduction in intensity. The spatial filter 20 is also aligned with theoptical axis and has a predetermined aperture so that it provides areduction in intensity within a predetermined numerical aperture.Herein, numerical aperture is defined in its normal manner as being adimensionless quantity characterising a range of angles with respect tothe sample location from which the output light originates.Specifically, the numerical aperture NA may be defined by the equationNA=n·sin(θ), where θ is the half angle of collection and n is therefractive index of the material through which the output light passes(for example the material of the components of the optical system 6).

The spatial filter 20 provides no intensity reduction outside thepredetermined numerical aperture. In principle, the spatial filter 20could alternatively provide a reduction in intensity outside itspredetermined aperture, but a reduction in intensity that is less thanthe reduction in intensity within the predetermined numerical aperture,although this is less desirable.

The spatial filter 20 may be formed in any suitable manner, typicallycomprising a layer of deposited material. The material may be, forexample, a metal such as silver. The deposition may be performed usingany suitable technique.

As sub-diffraction sized objects near an interface scatter lightpreferentially into a larger numerical aperture than the reflectedilluminating light, the reduction in intensity provided by the spatialfilter 20 preferentially reduces the intensity in detection of thereflected illuminating light over the scattered light. Accordingly, thereduction in intensity by the spatial filter 20 at low numericalapertures predominantly affects the reflected illuminating light and hasa minimal effect on the scattered light, thereby maximising the contrastin the capture image. The enhanced imaging contrast enables highcontrast detection of objects that are weak scatterers.

The contrast enhancement may be understood as follows. As the spatialfilter 20 passes part of the output light in the predetermined numericalaperture (i.e. is partially transmissive in this example), fractions ofilluminating light and scattered light fields reach the detector andinterfere for a sufficiently coherent illumination source. The lightintensity reaching the detector I_(det) is then given byI_(det)=|E_(inc)|²{r²t²+|s|²+2rt|s|cos Φ}, where E_(inc) is the incidentlight field, r² is the reflectivity of the interface and t² is thetransmissivity of the spatial filter 20, s is the scattering amplitudeof the object, and Φ is the phase difference between transmittedilluminating light and the scattered light. Thus, the scatteringcontrast is enhanced, albeit at the expense of the total number ofdetected photons.

Thus, contrast is provided in a similar manner to conventional iSCAT,but controlled additionally by the transmissivity of the spatial filter.This provides the ability to tune the amplitude of the reference fielddirectly through selection of the transmissivity t² of the spatialfilter 20 as opposed to being fixed by the reflectivity of a glass-waterinterface as in standard iSCAT. In the case that the spatial filter 20is a layer of deposited material, the transmissivity t² may be selectedby choice of the material and/or thickness of the layer. Such tuning maybe performed according to, for example, the scattering object ofinterest, the camera full well capacity and magnification.

The interferometric light scattering microscope may include a sampleholder that incorporates a solid immersion lens (SIL) as the finaloptical element. An SIL has higher magnification and higher numericalaperture than common lenses by filling the object space with ahigh-refractive-index solid material. It may be preferred to include ahemispherical SIL or a superhemispherical SIL. The SIL may be anysuitable SIL, and has preferably been polished such that the roughnesshad been reduced to less than 10 nm. Alternatively, the SIL can bemanufactured, for example by 3D printing, such that the roughness isless than 10 nm.

To maximise these beneficial effects to iSCAT, the predeterminednumerical aperture may be the numerical aperture of the reflectedilluminating light within the output light, but that is not essential.For example, benefits of a similar nature could be achieved if thepredetermined numerical aperture was slightly smaller than, or largerthan the numerical aperture of the reflected illuminating light.

Use of the spatial filter 20 does not fundamentally alter thesensitivity limits or SNR (signal to noise ratio) achievable forscattering by a given object, incident light intensity and exposuretime. However, by improving the contrast and reducing the overalldetected photon flux, it does, however, dramatically simplify theimplementation of iSCAT to achieve a given sensitivity or SNR. ExistingiSCAT microscopes have complex and expensive components, for examplerequiring an optical table, and expensive and complex optics,electronics, as well as needing expert operation. Such requirements aregreatly relaxed by the use of the spatial filter 20. Equivalentperformance to existing iSCAT microscopes may be achieved, for example,simply by adding the spatial filter 20 to an existing commercialmicroscope that does not have the complex and expensive componentsmentioned above. The spatial filter 20 itself is of course a simple andcheap component. In addition, the spatial filter 20 enables use ofstandard CMOS or CCD cameras with low full well capacity without loss ofimaging sensitivity.

Thus, the microscope 1 may be an existing commercial microscope, whichis adapted by incorporating the spatial filter 20. Such an adaptationcan be performed very cheaply and simply. The adaption may be performedby forming the spatial filter in an adaptor arranged to be received inan accessory slot of an existing commercial microscope, for example in asimilar manner to the adaptor disclosed in WO-2015/092778 that is usedto incorporate a mirror into a microscope.

Alternatively, the microscope 1 may be designed specifically for usewith the spatial filter 20.

An image acquired using an example of the microscope 1 is shown in FIG.2. In this example, coherent brightfield illuminating light was providedand the spatial filter 20 comprised by a layer of silver of thickness180 nm deposited on fused silica with a 3.5 mm diameter so as totransmit 1×10⁻² of the reflected light intensity. This results in ascattering contrast of 1% for a single 395 kDa protein and a SNR of 10(at an image capture rate of 10 frames s⁻¹, and with an intensity ofilluminating light of 10 kW/cm²). FIG. 2 is an image captured using alow cost CMOS camera as the detector 5. As can be seen, a high contrastimage is achieved. Moreover, brightfield illumination ensures that thestrongest unwanted back-reflections, usually originating from theobjective are directed away from the detector 5, minimising imagingbackground and enabling large fields of view without complex scanning ofthe beam of illuminating light.

The advantages of enhanced contrast allow imaging of objects thatscatter light so weakly that imaging with other techniques is difficult.For example, the present invention may be applied with advantage to asample comprising objects having a mass of 5000 kDa or less. Typically,the present invention may be applied to a sample comprising objectshaving a mass of 10 kDa or more, for example objects having a masswithin a range from 10 kDa to 5000 kDa.

Alternatively or as well, the present invention may be applied to asample comprising objects having a scattering cross section with respectto the illuminating light of 10⁻¹² m² or less, or more preferably 10⁻¹⁷m² or less. Typically such objects may also have a scattering crosssection with respect to the illuminating light. Typically such objectsmay also have a scattering cross section with respect to theilluminating light of 10⁻²⁰ m², or more preferably 10⁻² m² or more, forexample within a range from 10⁻¹⁷ m² from 10⁻²⁶ m². Scattering crosssection is a fundamental, measurable property relating to the effectivesize of an object to incident light of a particular wavelength,independent of the technique used to measure it. Scattering crosssections can be, for example, measured by dark field microscopy.

Examples of objects to which the present invention may be appliedinclude proteins or small aggregates thereof, or their binding partners.

In order to image objects that are relatively weak scatterers, thespatial filter 20 may be arranged to pass reflected illuminating lightwith a reduction in intensity within the predetermined numericalaperture to an intensity in the range from 10⁻² to 10⁻⁴ of the incidentintensity (in this context, the intensity of the output light that isincident on the spatial filter 20).

Otherwise, the microscope 1 may be designed and operated withoutreference to the spatial filter 20. For example, the field of view isadjustable by changing the focusing conditions of the illuminationlight. Similarly, multi-colour imaging requires no more than couplingadditional laser sources into a single mode fibre, if such a fibre isused to deliver the illumination light. In general terms, the microscope1 may be adapted to use other components and techniques known for iSCAT,for example as disclosed in Kukura et al., “High-speed nanoscopictracking of the position and orientation of a single virus”, NatureMethods 2009 6:923-935, and in Ortega-Arroyo et al. “Interferometricscattering microscopy (iSCAT): new frontiers in ultrafast andultrasensitive optical microscopy”, Physical Chemistry Chemical Physics2012 14:15625-15636.

Some examples of specific modifications that may be made to microscope 1will now be described with reference to FIGS. 3 to 5, although theseexamples are without limitation. Apart from the modifications describedbelow, the microscope 1 has the same construction and operation asdescribed above. For brevity, common components are given the samereference numerals, and the above description thereof is not repeated.

FIG. 3 illustrates the microscope 1 with a modification to position thespatial filter 20 at a conjugate focal plane 21 of the back focal plane15 of the objective lens 11, instead of being behind the back apertureof the objective lens 11. The conjugate focal plane 21 of the back focalplane 15 of the objective lens 11 is formed between a pair of telescopelenses 22 and 23 positioned behind the tube lens 13. The first telescopelens 22 in the optical path images the back focal plane 15 of theobjective lens 11 to form the conjugate focal plane 21 and the secondtelescope lens 23 images the conjugate focal plane 21 onto the detector5.

The spatial filter 20 is provided at the conjugate focal plane 21 and isformed on a transparent plate 24. The configuration and operation of thespatial filter 20 are the same as described above with reference to FIG.1, for example being aligned with the optical axis and having apredetermined aperture so that it provides reduction in intensity withinthe same, predetermined numerical aperture as described above (althoughthe spatial filter 20 is now nearly perpendicular to the optical path,rather than at 45° to the optical path).

FIG. 4 illustrates the microscope 1 with a modification in which thespatial filter 20 is reflective, instead of being transmissive. In thismodification, the positions of the light source 4 and the detector 5 arereversed so that the illuminating light from the light source 4 istransmitted through the beam splitter 14 into the objective lens 11, andconversely the output light from the sample location is reflected by thebeam splitter 14 towards the detector 5.

The spatial filter 20 is formed on the beam splitter 14, but in view ofthe reversal of the light source 4 and the detector 5, the spatialfilter 20 is reflective. Despite being reflective, the spatial filter 20is arranged to operate in the same manner as described above. That is,the spatial filter 20 filters the output light passing to the detector 5passes the output light but with reduction in intensity. Althoughachieved in this case by being partially reflective, the configurationand operation of the spatial filter 20 is otherwise the same, forexample being aligned with the optical axis and having a predeterminedaperture so that it provides reduction in intensity within apredetermined numerical aperture, as described above.

FIG. 5 illustrates the microscope 1 with a modification similar to thatof FIG. 3 to position the spatial filter 20 at a conjugate focal plane25 of the back focal plane 15 of the objective lens 11, instead of beingbehind the back aperture of the objective lens 11, and with a furthermodification in which the spatial filter 20 is reflective, instead ofbeing transmissive. The conjugate focal plane 25 of the back focal plane15 of the objective lens 11 is formed between a pair of telescope lenses22 and 23 positioned behind the tube lens 13, in the same manner as inthe modification of FIG. 3. However, a reflective plate 26 is providedbetween the telescope lenses 22 and 23 at the conjugate focal plane 25but arranged at 45° to deflect the optical path so that the reflectionat the reflective plate 26 diverts the optical path by 90°. The spatialfilter 20 is provided at the conjugate focal plane 25 by being formed onthe reflective plate 26, and so is reflective instead of transmissive.Despite being reflective, the spatial filter 20 is arranged to operatein the same manner as described above, that is in a similar manner tothe modification of FIG. 4.

FIG. 6 illustrates the microscope 1 with a modification to position thespatial filter 20 at a conjugate focal plane 21 of the back focal planeof the objective lens 11, instead of being behind the back aperture ofthe objective lens 11. The conjugate focal plane 21 of the back focalplane 15 of the objective lens 11 is formed between a pair of telescopelenses 22 and 23 positioned behind the tube lens 13, in the same manneras in the modification of FIG. 3. However, compared to the modificationof FIG. 3, the following further modifications are also made, notingthat each of the modifications in FIG. 6 could be applied independentlyof each other.

An acousto-optical deflector 32 is arranged after the light source 4 toprovide scanning of the illuminating light. The acousto-opticaldeflector 32 may be operated to scan a region of the sample 3 to buildup an image and/or to provide spatial modulation for removing specklepatterns that arise from the coherent nature of the illumination andlaser noise, as mentioned above.

The condenser lens 12 is replaced by a pair of telecentric lenses 30 and31 that perform the function of imaging any modifications to the beampath at the acousto-optical deflector 32 into the back focal plane ofthe imaging objective.

The positions of the light source 4 and the detector 5 are reversed in asimilar manner to the modification of FIG. 4, so that the illuminatinglight from the light source 4 is transmitted through the beam splitter14 into the objective lens 11, and conversely the output light from thesample location is reflected by the beam splitter 14 towards thedetector 5.

The beam splitter 14 is a polarising beam splitter and a quarter waveplate 33 is arranged between the beam splitter 14 and the sample 3, sothat the beam splitter 14 splits the light.

A mirror 34 is arranged to deflect the output light reflected by thebeam splitter 14. This is merely to provide for a more compactarrangement of the microscope 1.

The microscope 1 may be used to perform iSCAT for a wide range ofapplications including single molecule detection. In general, thecontrast enhancement is beneficial and may be applied to all imaging ofsub-diffraction and weakly scattering objects. A particular applicationis label-free imaging of weak scatterers, where objects of interest haveto be invariably detected on top of a large background, which reducesthe imaging contrast. The microscope 1 may be used for a wide range ofstudies and measurements, for example to measure any changes inrefractive index, which includes, for example: single moleculebinding/unbinding, phase transitions, clustering, assembly/disassembly,aggregation, protein/protein interactions, protein/small moleculeinteractions, high-sensitivity label-free imaging.

Thus, there are numerous applications for the microscope 1, ranging fromfundamental research to industrial applications, for example in thepharmaceutical industry. In particular, it opens up iSCAT to fieldsprecluded by the complex experimental setups currently needed to performiSCAT. As an example, iSCAT is currently the world's most sensitivelabel-free single molecule imaging biosensor, which could havesignificant impact for example on the surface plasmon resonance sensingmarket. In addition, it functions as an accurate, precise and highlyresolved single molecule mass spectrometer in solution, with manyapplications in research and industry.

EXAMPLES

Important performance parameters of accuracy, resolution and precisionhave been quantified using a series of protein samples in a microscopehaving the configuration shown in FIG. 6, as follows.

Quantification of the instrumental accuracy in determining molecularweight was performed by recording the scattering contrasts of a seriesof proteins dissolved at 10 nM concentration in standard PBS buffer (theproteins being Streptavidin—53 kDa, Bovine Serum Albumin—66 kDa, AlcoholDehydrogenase—˜146 kDa, β-Amylase—224 kDa, HSP 16.5-395 kDa, non-musclemyosin IIb modified with a HALO tag—598 kDa, Thyroglobulin—669 kDa,GroEL—802 kDa). The results are shown in FIG. 7, which is a plot ofscattering contrast vs sequence molecular weight and associated errorbars. The results exhibit highly linear behaviour as expected given thelinear dependence of iSCAT contrast on the volume of the object and thefact that all proteins are made from the same pool of amino acids, whichin turn have a common refractive index and thus scattering crosssection. The observed variation between expected and measured molecularmass is on average on the order of 3% of the mass that is to bedetermined. This demonstrates a high degree of accuracy in determiningmolecular weight of single protein molecules using the microscope 1.

It is anticipated that the microscope 1 is capable of quantifying themass of objects as small as single proteins with an accuracy better than5% of its mass, irrespective of their composition in terms of aminoacids, lipids or carbohydrates.

It is anticipated that the microscope 1 is capable of quantifyingchanges in the mass of objects smaller than existing techniques, forexample having masses below 5000 kDa and down to 10 kDa. Quantificationof the instrumental precision was performed as follows. The precision indetermining the mass of an object using the approach described herein isstatistically limited by the ability to determine the centre of thedistribution of scattering contrasts. Given that the recordeddistribution exhibit Gaussian profiles as expected for a shot noiselimited process, the precision is well-known to scale as sN^(−1/2),where s is the standard deviation of the distribution of interest and Nthe number of samples taken. As a result, the precision of massmeasurement is not limited by the resolution or accuracy, but can inprinciple be arbitrarily increased by increasing the number ofmeasurements. To illustrate this, mass histograms of avidin wererecorded in the absence (6839 events) and presence (6609 events) ofsaturating biotin in solution and the results are shown in FIGS. 8 and9, respectively. A mass increase in the presence of biotin of 950±330 Dawas measured compared to the expected 970 Da for four biotin moleculesbound.

Quantification of the instrumental resolution and in particular theresolution limits, was performed by recording a mass histogram using thecalibration obtained from the results illustrated in FIG. 6 of bovineserum albumin. The resulting histogram is shown in FIG. 10 and exhibitsindividual discernible peaks for monomers, dimers and trimers insolution with a fwhm of the monomer peak of 28 kDa leading to aresolution of 34 kDa. This value is a function of the total number ofdetected photons and can thus be improved by using higher lightintensities. This demonstrates a high degree of mass resolution for themicroscope 1. Taken together, these results illustrate that themicroscope 1 is capable of quantifying mass differences as small asthose induced by the binding of small molecules and both accurate andprecise characterisation of an objects mass via the polarisability.Currently, this enables detection of single proteins down to 40 kDa andaccurate determination of their molecular mass to within 5% of theirsequence mass (FIG. 7). The high SNR furthermore enables thecharacterisation of changes in mass, for example through binding eventswith a precision that is only limited by the number of detectedmolecules, currently reaching 250 Da (FIGS. 8 and 9). In addition, thehigh achievable SNR enables the clear detection of different oligomericstates of proteins in solution enabling detailed characterisation ofprotein aggregation (FIG. 10).

Accordingly, the invention relates to a method of quantifying the massof an object, wherein the mass of said object is quantified byinterferometric light scattering. The method may also be described asinterferometric light scattering mass spectrometry. The mass istypically quantified with up to about 5% mass error. By “mass error” ismeant the % difference between the quantified mass using interferometriclight scattering, and the actual mass of the object. The actual mass ofthe object may also be referred to as the “sequence mass” i.e. the masscalculated based on the sequence of the object molecule, which may be inkDa. Preferably, the mass is quantified with equal or less than about 2%mass error. The mass may be quantified with about 0.5% to up to about 5%mass error, about 1% to about 5% mass error, about 1.5% to about 5% masserror, about 2% to about 5% mass error, about 0.1% to about 2% masserror, about 0.5% to about 2% mass error, or about 1% to about 2% masserror.

The mass may be quantified within 5 kDa of, within 3 kDa of, within 2kDa of, preferably within 1 kDa of the actual mass (e.g. sequence mass)of the object.

The object whose mass is to be quantified, such as quantified with amass error described above, is typically of 19 kDa or greater in size.The object may more generally be from 10 kDa to 5000 kDa in size.

The object may be a single molecule, a macromolecule, a supermolecule oran association of molecule, macromolecules (such as polymers) andsupermolecules. Examples of suitable macromolecules includes nucleicacid molecules, either natural nucleic acids such as deoxyribonucleicacid (DNA) or ribonucleic acid (RNA), or artificial nucleic acids suchas peptide nucleic acid (PNA), Morpholino and locked nucleic acid (LNA),as well as glycol nucleic acid (GNA) and threose nucleic acid (TNA).Associations of molecules can include assemblies such as virus-likeparticles where envelope or capsid proteins are associated.

The object may be a weak scatterer of light. The object may be a singleprotein, and may be a glycoprotein. The object is typically in solution.The method of quantifying mass of the invention typically comprises useof an interferometric scattering microscope of the invention.

The invention further provides a method of measuring or quantifying achange in the mass of an object, wherein the change in mass of saidobject is measured or quantified by interferometric light scattering.The change in mass of the object may be quantified with any mass erroras described above. The mass of the object before and after the changein mass is typically in a mass range as described above. The mass of theobject may change due to one or more events selected from the groupconsisting of single molecule binding/unbinding, phase transition,clustering, assembly/disassembly, aggregation, one or moreprotein/protein interactions and/or one or more protein/small moleculeinteractions. The mass of the object may change due to oligomericassembly or glycoprotein cross-linking. The change in mass of the objectmay be time-resolved (i.e. measured or quantified as occurring over agiven period of time), optionally measured or quantified at a specificposition and/or local concentration of said object. The use ofinterferometric light scattering for measuring changes in the mass ofindividual objects in a position and local concentration sensitivemanner is also provided.

The method may allow for one or more interactions resulting in change inthe mass of the object to be quantified. The method may further comprisedetermining thermodynamic and/or kinetic parameters influencing thechange in the mass of the object or of one or more interactionsresulting in change in the mass of the object. The method of quantifyingchange in mass of an object of the invention may comprise use of aninterferometric scattering microscope of the invention.

The use of interferometric light scattering for quantifying mass ofobjects such as proteins and measuring/quantifying changes of mass ofsuch objects (for example based on molecular interactions) was furtherinvestigated. Light-scattering-based imaging of individual biomoleculesallowed the spatiotemporal characterization of their interactions andassembly.

Interferometric scattering microscopy was used to quantify the mass ofsingle biomolecules in solution with 2% sequence mass accuracy, up to19-kDa resolution, and 1-kDa precision. Interferometric light scatteringwas used resolve oligomeric distributions at high dynamic range, detectsmall-molecule binding, and mass-image proteins with associated lipidsand sugars (carbohydrates). These capabilities enabled characterizationof the molecular dynamics of processes as diverse as glycoproteincross-linking, amyloidogenic protein aggregation, and actinpolymerization. Interferometric scattering mass spectrometry providedspatio-temporally resolved measurement of a broad range of biomolecularinteractions, one molecule at a time. This data is shown in theExamples.

The cellular processes underpinning life are orchestrated by proteinsand their interactions. The associated structural and dynamicheterogeneity, despite being key to function, poses a fundamentalchallenge to existing analytical and structural methodologies.Biomolecular interactions and assembly are central to a wide range ofphysiological and pathological processes spanning length scales fromsmall complexes to the mesoscale. Despite considerable developments intechniques capable of providing high-resolution structural information,they are typically static and involve averaging over many molecules inthe sample, and therefore often do not fully capture the diversity ofstructures and interactions made. Solution-based ensemble methods enabledynamic studies but lack the resolution of separation required todistinguish different species. Single molecule methods offer a means tocircumvent heterogeneity in both structure and dynamics, and significantprogress has been made in terms of characterizing interactions andmechanisms. There existed no single-molecule approach, however, capableof quantifying and following the diversity of interactions made bybiomolecules with sufficient spatiotemporal accuracy and resolution.

Given sufficient sensitivity, we viewed light scattering as an idealmeans for detecting and characterizing molecules in low-scattering invitro conditions because of its universal applicability. In aninterferometric detection scheme (FIG. 10A), the scattering signalscales with the polarizability, which is a function of the refractiveindex and proportional to the particle volume. Combining theapproximation that single amino acids effectively behave like individualnano-objects with the observation that the specific volumes of aminoacids and refractive indices of proteins vary by only ˜1% (FIG. 14;Table S1) suggests that the number of amino acids in a polypeptide, andthus its mass, is proportional to its scattering signal. This closerelationship between mass and interferometric contrast, which has beenpredicted and observed to hold coarsely even at the single moleculelevel, could thus in principle be used to achieve high mass accuracy.

Building on recent advances in the experimental approach (FIG. 15) thatimproved imaging contrasts for interferometric scattering microscopy, wecould obtain high quality images of single proteins as they diffuse fromsolution to bind non-specifically near the interface consisting of amicroscope coverslip and the solution (FIG. 6). Reaching signal-to-noiseratios>10, even for small proteins such as bovine serum albumin (BSA),combined with an optimized data analysis approach, allowed us to extractthe scattering contrast for each molecular binding event with highprecision (FIG. 10C). These led to clear signatures of differentoligomeric states, shown here for BSA with relative abundances of88.63%, 9.94%, 1.18% and 0.25% of the detected particles (FIG. 10D). Fornon-specific binding to an unfunctionalized microscope coverslip as usedhere, surface attachment was effectively irreversible (12209 binding vs372 unbinding events). As a result, we could determine (bulk) bindingrate constants, which generally exhibited only small variations witholigomeric state that could be accommodated to obtain minor correctionsto the recorded mass spectra and yield the solution distribution (FIG.16). These results, including the detection and quantification of rarecomplexes such as BSA tetramers, demonstrate the ability ofinterferometric light scattering to work as interferometric scatteringmass spectrometry (e.g. iSCAMS) to characterize solution distributionsof oligomeric species and molecular complexes at high dynamic range.

The regular spacing in the contrast histogram of BSA confirms theexpected linear scaling between mass and interferometric contrast.Repeating these measurements for eight different proteins, spanning53-803 kDa, revealed a linear relationship (FIG. 11A, FIG. 18A). Thedeviation between measured and sequence mass was <5 kDa, resulting in anaverage error of 1.9%, and no detectable correlation with refractivityin relation to the overall shape of the molecule (FIG. 19A). Even forlarge structural differences, such as those between the extended andfolded conformation of smooth-muscle myosin (530.6 kDa, FIG. 11A andFIGS. 18B and 20), we did not find measurable differences in themolecular mass beyond the mass increase expected for addition ofglutaraldehyde molecules (Extended: 528.4±16.2 kDa, folded: 579.4±14.8kDa, FIG. 18B) used to crosslink myosin into the folded conformation.The resolution, as defined by the full-width at half-maximum (FWHM) ofthe measured contrast reached 19 kDa for streptavidin. In all cases, theresolution was limited by photon shot noise and influenced by molecularmass, increasing from 19 kDa for streptavidin to 102 kDa forthyroglobulin (FIG. 19B, C). The sub-0.5% deviation from sequence massfor species of >100 kDa compares well to native mass spectrometry, anddemonstrates the intrinsic utility of interferometric light scattering(e.g. iSCAMS) for the accurate mass measurement of biomolecules witholigomeric resolution.

Moving beyond species composed solely of amino acids, lipid nanodiscsrepresent an ideal system for testing the broad applicability ofinterferometric light scattering (e.g. iSCAMS) due to their flexibilityin terms of polypeptide and lipid content. For nanodiscs composed of theMSP1D1 belt protein and DMPC lipids, we obtained a mass of 141.0±1.6kDa, in good agreement with the range of masses reported by othermethods, spanning 124-158 kDa (FIG. 11B and FIG. 17D). Replacing MSP1D1with the smaller MSP1ΔH5 reduces the nanodisc diameter and the lipidcontent by ˜20%, after accounting for the thickness of the protein belt.Given the masses of MSP1D1 and MSP1ΔH5 (47 and 42 kDa, respectively), wepredicted a mass for the MSP1ΔH5 nanodisc of 113.6 kDa, in excellentagreement with our measurement (114.1±1.9 kDa).

To see whether our approach also applies to solvent-exposed moietiesthat experience a different dielectric environment to those buriedwithin a protein, we selected the HIV envelope glycoprotein complex(Env), which is a trimer of gp41-gp120 heterodimers. Env is extensivelyN-glycosylated, with the carbohydrates contributing to almost half ofits mass. For an Env trimer mimic expressed in the presence ofkifunensine, a mannosidase inhibitor that leads predominantly tounprocessed Man9GlcNAc2 glycans (FIG. 21), we recorded a mass of350.0±5.7 kDa. Making the crude approximation that glycans andamino-acids have similar polarizabilities, this corresponds to a glycanoccupancy of 74±3 out of 84 possible sites (FIG. 2C and FIG. 18E),consistent with recent observations of high occupancy for gp120expressed with kifunensine. For Env expressed without kifunensine werecorded a lower mass of 315.3±10.5 kDa. The mass difference can only inpart be attributed to the lower average mass of the processed glycansand yields a total N-glycan occupancy of 61±6. While the exact valuesfor occupancy are beholden to our calibration (FIG. 11A), the presenceof unoccupied sites is consistent with their observation in proteomicsdata.

The high precision of 1.8±0.5% with respect to the protein mass (FIG.11A), indicates the potential for direct detection of small-moleculebinding. To probe the current limits of interferometric light scattering(e.g. iSCAMS) in terms of precision, we therefore examined thebiotin-streptavidin system (FIG. 11D, FIG. 18C), and measured masses forstreptavidin in the absence (55.7±1.1 kDa) and presence (57.4±0.9 kDa)of biotin. This corresponds to a difference of 1.7±1.4 kDa, in goodagreement with the expected 0.98 kDa for complete occupancy of the fourbinding sites. Upon addition of two different biotinylated peptides(3705.9 Da and 4767.4 Da), we obtained increases of 16.1±2.8 kDa and22.0±2.2 kDa (compared to 14.8 kDa and 19.1 kDa expected) (FIG. 11D).These data show that interferometric light scattering (e.g. iSCAMS) candetect the association of kDa-sized ligands, demonstrating itssuitability for highly sensitive ligand-binding studies in solution.

After having established the capabilities of iSCMAS, we sought to testit on more complex systems that are difficult to assess quantitativelywith existing techniques as a consequence of heterogeneity andmulti-step assembly mechanisms (FIG. 12). In addition, we aimed tomonitor nucleation and polymerization dynamics of mesoscopic structuresdown to the single molecule level, which are challenging because of thesimultaneous requirement for high dynamic range, imaging speed anddirect correlation between the observed signals and the associatedmolecular events. The biotin-streptavidin system exhibits nearlycovalent binding, raising the question whether interferometric lightscattering (e.g. iSCAMS) is capable of not only determining massdistributions but also of quantifying weaker equilibria, as oftenencountered for protein-protein interactions.

We therefore investigated the interaction of Env with the anti-virallectin BanLec, which neutralises HIV by binding to surface N-glycans viaan unknown mechanism. We could monitor the interactions and short-livedcomplexes prior to aggregation, with the addition of BanLec to Envresulting in a reduction of single Env units coupled to the appearanceof dimers and higher-order assemblies (FIG. 13A). The experimentaloligomeric evolution coupled with a simple model (FIG. 13B) enabled usto extract the underlying association constants (K_(BanLec)=0.12 nM⁻¹,K_(Env)=8 nM⁻¹, K_(‘BanLec)=0.4 nM⁻¹), in good agreement with recentbulk studies (K_(BanLec)=0.19 nM−1), which also observed signatures ofand estimated the energetics of a secondary binding event (K2=2.85nM⁻¹). Our ability to follow and model the evolution of differentoligomeric species allowed us to directly extract the interactionmechanism and the energetics underlying the lectin-glycoproteininteraction, despite the heterogeneity of this multi-component system.As a result, we can show that binding of Env to BanLec that is alreadybound to Env is much stronger than to free BanLec, a key characteristicof cooperative behavior. Moreover, the mass resolution of this approachenabled the inventors to quantify the number of BanLecs bound per dimer(1-2), trimer (2-3) and tetramer (3-4) of Env, demonstrating bivalentbinding. These results are directly relevant to the characterization andoptimization of anti-retrovirals, given that multivalency andaggregation have been proposed to be directly linked to neutralizationpotency. We anticipate similar quantitative insights to be achievablefor other therapeutic target proteins and protein-protein interactionsin general.

An advantage of our imaging-based approach stems from its ability totime-resolve mass changes. These mass changes can be resolved in aposition- and local concentration-sensitive manner. Further, the mass ofmultiple objects can be measured over time, preferably in alocation-dependent manner. The location may be sub-diffraction precise.This approach may enable the detection and/or quantification in thechange of mass when these are due to the binding and/or dissociation ofbinding partners. Such results will give an indication and insight ofthe binding constants, k_(on) and k_(off). Alternatively, the approachmay enable the determination of binding events which can be correlatedto a single molecule fluorescence localization measurements to identifyspecific binding partners labelled with fluorescent molecules. Further,the addition of a molecule of known specificity (e.g. aptamer, antibody,antibody fragment or derivative, affibody, lectin, antigen or toxin) canbe used to identify the nature of the molecule under observation.

The measurements of mass changes enables us to examine surface-catalyzednucleation events that may eventually lead to amyloid formation.Previous studies using fluorescence labeling found aggregates of ˜0.6 μmdiameter within a minute of addition of the amyloidogenic proteinα-synuclein at 10 μM to an appropriately charged bilayer. Upon addingα-synuclein to a planar, negatively charged DOPC/DOPS (3:1) membrane atphysiological pH, we observed the appearance and growth of nanoscopicobjects within seconds, even at low μM concentrations (FIG. 13A). Whilewe were unable to determine the sizes of initial nucleating species orindividual assembly steps, given the low molecular mass of α-synuclein(14 kDa), we could nevertheless monitor the nanoscale formation ofassociated structures in the range of hundreds of kDa and determine thekinetics (FIG. 13B). Growth of these clusters was uniform across thefield of view, with the initial rates following expectations for afirst-order process (FIG. 13B and FIG. 22A), pointing towards a simplegrowth mechanism. We did not detect such structures on neutral,DOPC-only bilayers, and found evidence for thioflavin-T positiveaggregates after overnight incubation (FIG. 22B), suggesting that ourassay probes early stages of amyloid assembly.

At the extremes of our current sensitivity, interferometric lightscattering (e.g. iSCAMS) enables mass-imaging of mesoscopicself-assembly, molecule-by-molecule. In an actin polymerization assay,subtraction of the constant background revealed growth ofsurface-immobilized filaments. In contrast to α-synuclein, where thegrowth of interest took place within a diffraction-limited spot, here wecould quantify length changes of filaments larger than the diffractionlimit upon the attachment and detachment of actin subunits (FIG. 13C,Fig). We observed distinct, step-wise changes in the filament length(FIG. 13D), the most frequent forward and backward step sizes in thetraces being 3.0±0.8 nm and 2.7±0.7 nm, respectively, remarkably closeto the expected length increase of 2.7 nm upon binding of a single actinsubunit to a filament (FIG. 13E). Detection of larger step sizesrepresents the addition of multiple actin subunits within our detectiontime window. The contrast changes associated with the different stepsizes corresponded to mass changes of one, two, or three actin monomersbinding to and unbinding from the tip of the growing filaments duringacquisition. Even though we cannot yet distinguish between modelsinvoking monomer or oligomer addition to a growing filament at ourcurrent level of spatio-temporal resolution, these results demonstratethe capability of interferometric light scattering (e.g. iSCAMS) forquantitatively imaging mesoscopic dynamics, and how they are influencedby associated proteins at the single molecule level.

We anticipate that combining interferometric light scattering (e.g.iSCAMS) with established surface modifications will dramatically expandits capabilities. Passivation decreases surface binding probabilitiesand thereby should provide access to much higher analyte concentrations(>μM), while surface activation will reduce measurement times at lowconcentrations (<nM). Specific functionalization and immobilization ofindividual subunits or binding partners could also allow for thedetermination of on and off rates in addition to equilibrium constants,and enable targeted detection in the presence of other analytes. Theseadvances will make interferometric light scattering (e.g. iSCAMS) apowerful approach for dynamic in vitro studies of biomolecularinteractions, assembly and structure at the single molecule level.

Materials and Methods Protein Volume and Refractive Index Calculation

All protein sequences from Escherichia coli, Yersinia pestis, Haloferaxvolcanii, Methanocadococcus jannaschii, Homo sapiens, and Arabidopsisthaliana genomes were downloaded from NCBI(https://www.ncbi.nlm.nih.gov/genome/) in FASTA format. The refractiveindex of each protein was calculated as

$n = \sqrt{\frac{( {\frac{2{MR}}{VN} + 1} )}{( {1 - \frac{R}{V}} )}}$

where M=M_(w)+Σ_(i)(M_(i)−M_(w)) is the molar mass of the protein,determined from the sequence, and where the M_(w)-terms represent theloss of one water molecule per peptide bond; R=Σ_(i)n_(i), is the sum ofcontributions from individual amino acid residues; V=Σ_(i)V_(i), thespecific volume, also determined from the contribution of individualresidues; and N is the number of residues. M_(i), n_(i), and V_(i) weredetermined from the residue type (Table S1). Wherever ambiguous FASTAcodes were encountered (B, J, X, and Z), parameters (M_(i), n_(i), andV_(i)) were set to the arithmetic average for the possible residue types(e.g., the average of D and N for FASTA code B).

Microscope Coverslip Cleaning Procedure

We cleaned borosilicate microscope coverslips (No. 1.5, 24×50 mm², VWR,and No. 1.5, 24×24 mm², VWR) by rinsing them sequentially with H₂O,ethanol, H₂O, isopropanol, H₂O, ethanol and H₂O, followed by dryingunder a clean stream of nitrogen.

Landing Assay Procedure

Cleaned coverslips were assembled into flow chambers. Buffers werefiltered through a 0.2 μm pore size syringe filter. All samples werediluted from stock solutions without further treatment. Sample proteinswere diluted in 20 mM Tris-HCl, 100 mM NaCl, pH 7.4, unless otherwisestated. Typical working concentrations were 5-10 nM of the predominantspecies. After filling the flow chamber with buffer, a clean region ofinterest in the flow chamber was selected defined as being devoid oflarge scatterers on the surface, followed by flushing in 10 μl of theprotein solution.

Calibration Proteins

Streptavidin (2105), bovine serum albumin (BSA, 2571), alcoholdehydrogenase (ADH, 8066), β-amylase (βA, 9988), thyroglobulin (2951)and GroEL (3955) were purchased from Sigma-Aldrich. Non-muscle myosin 2B(10042) was purified as described previously, except for the addition ofa halo-tag. The numbers in parentheses indicate the total number ofdetected particles for each calibrant from 4-10 separate experiments.

Myosin Crosslinking

Smooth-muscle myosin (SMM) was purified as described previously andincubated at 200 nM in ATP-containing buffer (10 mM MOPS, 150 mM NaCl, 1mM MgCl₂, 0.1 mM EGTA, 0.1 mM ATP, pH 7.0) for 30 min, which induced thefolded (OS) conformation. Glutaraldehyde was added to a concentration of0.1% (v/v) and incubated for 1 min. The reaction was stopped by additionof Tris-HCl (pH 8.0) to a final concentration of 100 mM.

Electron Microscopy

Cross-linked myosin was diluted to 20 nM in buffer containing 10 mMMOPS, 150 mM NaCl, 1 mM MgCl₂, 0.1 mM EGTA, pH 7.0. Native myosin wasdiluted to 5 nM in buffer containing 10 mM MOPS, 500 mM NaCl, 1 mMMgCl₂, 0.1 mM EGTA, pH 7.0, which induced the extended conformation. 3μl of sample was applied to a carbon-coated copper grid (pretreated for45 minutes with ultraviolet light) and stained with 1% uranyl acetate.Micrographs were recorded on a JEOL 1200EX II microscope operating atroom temperature. Data were collected on an AMT XR-60 CCD camera.

Interferometric Light Scattering (e.g. iSCAMS) Measurements ofSmooth-Muscle Myosin

Native (6S) and cross-linked (10S) myosins were diluted in buffercontaining 10 mM MOPS, 500 mM NaCl, 1 mM MgCl₂, 0.1 mM EGTA, pH 7.0, toa concentration of 5 nM and kept on ice until use. The landing assay wasperformed in a flow chamber as described above.

Biotin-Streptavidin Binding Assay

Streptavidin (Cat no. S4762) and D-biotin (Cat no. B4501) were purchasedfrom Sigma Aldrich. Two synthetic N-terminally biotinylated peptidesbased on the sequences of desmoglein-3 (DSG3,biotin-EWVKFAKPCREGEDNSKRNPIAKITSDYQA) and adrenocorticotropic hormone(ACTH, biotin-SYSMEHFRWGKPVGKKRRPVKVYPNGAEDESAEAFPLEF) were bought fromCambridge Research Biochemicals. Samples of 5 nM streptavidin and mixesof 5 nM streptavidin with 500 nM of either biotin, biotin-DSG3 peptideor biotin-ACTH peptide were prepared at the start of the day and kept onice until use. The total number of detected particles in 4-9 experimentswere 2105, 2167, 3131 and 936 for streptavidin, biotin-streptavidin,biotin-DSG3-streptavidin and biotin-ACTH-streptavidin, respectively.

Experimental Setup

The experimental setup is depicted schematically in FIG. 6, and isidentical to that described in FIG. 4 of Cole et al. (ACS Photonics. 4,211-216 (2017)), except for the apparatus being mounted onto a400×600×50 mm³ aluminium plate and enclosed to minimize the influence ofexternal perturbations. Briefly, the collimated output of a 445 nm laserdiode (Lasertack) is passed through an orthogonal pair of acousto-opticdeflectors (AODs; AA Opto Electronic, DT SXY-400). A 4f telecentric lenssystem (Telecentric lens 1, and Telecentric lens 2) images thedeflection of the beam by the AODs into the back focal plane of themicroscope objective (Olympus, 1.42 NA, 60×) after passing through apolarizing beam splitter (PBS) and a quarter-wave-plate (QWP). Thisresults in a weakly focused beam (spot size 1.5 μm) being scanned acrossthe sample to generate the field of view. The objective collects thelight reflected at the glass-water interface together with thatback-scattered by the sample, which is separated from the incident lightby the combination of the PBS and QWP. A second 4f telecentric system(Lens 1 and Lens 2) reimages the back focal plane of the objective,where a partially reflective mirror consisting of a 3.5 mm diameter thinlayer of silver deposited onto a window selectively attenuates thereflected light by more than two orders of magnitude with respect tolight from point scatterers at the surface. A final lens (Lens 3) imagesthe sample onto a CMOS camera (Point Grey GS3-U3-23S6M-C) with 250×magnification, giving a pixel size of 23.4 nm/pixel. The focus positionis stabilized with an active feedback loop using a totalinternally-reflected beam (not shown).

Data Acquisition Parameters

The camera was run close to the highest frame rate achievable for thegiven field of view, typically 1 kHz. Unless otherwise stated, imageswere pixel-binned 3×3 and time-averaged 10-fold prior to saving, givinga final pixel size of 70.2 nm and effective frame rate of 100 Hz. Thepower density, frame rate, exposure time and effective exposure timeafter averaging were: FIGS. 1B-D, 2D, S3, S4A and E, S5C, S6B: 860kW/cm², 1000 Hz, 0.95 ms, 47.5 ms; FIGS. 2A, 3, S4F, S5A and E, S6A andC: 420 kW/cm², 662 Hz, 1.5 ms, 300 ms; FIG. S5B: 500 kW/cm², 662 Hz, 1.5ms, 300 ms; FIGS. 2B and C, S5D and E: 280 kW/cm², 662 Hz, 1.5 ms, 300ms; FIGS. 13A and B, S22A: 45 kW/cm², 100 Hz, 9.9 ms; FIGS. 13C-E: 88kW/cm², 468 Hz, 2.1 ms, 16.8 ms.

Image Processing: Background Removal

Unless otherwise stated, analysis was performed using custom softwarewritten in LabVIEW. To remove the static scattering background from theglass surface, ratiometric images, R, were calculated asR=N_(m+1)/N_(m)−1, where N_(m) are consecutive normalized averages ofseveral images, revealing only those features that change between thetwo frame batches (FIG. 15B). Each frame batch is normalized by the meanpixel value before generating the ratiometric image to avoid effectscaused by slow laser intensity fluctuations. This processing is steppedthrough the raw movie frame-by-frame, generating a ratiometric framestack in which a binding event appears as a (dark) point spread function(PSF), the contrast of which increases and then decreases as themidpoint of the two frame batches approaches and then moves past thetime at which the protein binds (FIG. 15C, D). Unbinding events,meanwhile, appear as bright spots and are insignificant in numbercompared to binding events for landing assays on bare glass. Forexample, for the data shown in FIG. 10, we observed 12209 binding vs 372unbinding events.

Image Processing: Particle Detection and Quantiftcation

Particles were identified in the ratiometric images by an automated spotdetection routine. As a first step, the convolution of the ratiometricimage with the experimentally measured PSF was calculated to assist withparticle detection. From the resulting image, a particle probability(PP) image was calculated as described previously, and pixels withPP>0.3 that also corresponded to a local maximum in the convolved imagewere taken as candidate particles.

About each candidate pixel, an 11×11 pixel² (772×772 nm²) region ofinterest was extracted and fit to a model PSF to extract the contrast.In place of the more conventional 2D Gaussian function, we used adifference of two concentric 2D Gaussians to model the effect of thecircular partial reflector in the Fourier plane on the PSF. The widthand amplitude of the second Gaussian (arising from the presence of thepartial reflector) were dictated by the relative sizes of the partialreflector and objective back aperture, and the reflectance of the mask,thus avoiding additional parameters in the fit:

${f( {x,y} )} = {{A( {e^{- {\lbrack{\frac{{({x - x_{0}})}^{2}}{2\; \sigma_{x}^{2}} + \frac{{({y - y_{0}})}^{2}}{2\; \sigma_{y}^{2}}}\rbrack}} - {\frac{( {1 - T} )}{s}e^{- {\lbrack{\frac{{({x - x_{0}})}^{2}}{2{({s\; \sigma_{x}^{2}})}} + \frac{{({y - y_{0}})}^{2}}{2{({s\; \sigma_{y}^{2}})}}}\rbrack}}}} )} + b}$

where s=8.52/3.5, is the ratio between the diameter of the objectiveback aperture and the diameter of the partial reflector, and T is thetransmission of the mask. The contrast reported is therefore

${A( {1 - \frac{( {1 - T} )}{s}} )},$

corresponding to the peak value of this function as it appears in animage. If the fitted function was too eccentric it was rejected as notarising from a single molecule binding event. This was determined bytaking the ratio of the smaller to the larger of the two fitted standarddeviations (σ_(x) and σ_(y)), and rejecting the fit if this ratio wasbelow 0.7.

As described above, the sliding ratiometric analysis results in a singlemolecule binding event appearing in several consecutive frames, withincreasing and then decreasing contrast. To avoid over-countingparticles, and to extract the most accurate measure of the particlecontrast, the fits were grouped into those arising from a singleparticle based on their spatial and temporal location in the imagestack. Points lying within 1 pixel of each other and arising from frameswithin a window size of twice the temporal frame averaging wereclassified as one particle. The contrast of a given particle as afunction of time in the image stack then exhibits a linear growth up toa maximum, followed by a linear decrease. For each particle, thisprofile was fit to a pair of straight lines with gradients of equalmagnitude but opposite sign, and the peak contrast taken to be the bestestimate of the true particle contrast (FIG. 15D).

To extract accurate values for the mean contrast (FIG. 11), theresulting contrast distribution was fit to one or the sum of twoGaussian peaks (a Gaussian function when a single peak was well-isolatedfrom other detected species, or the sum of two Gaussians where two peakswere not fully separated). Fitting was performed using themaximum-likelihood procedure as implemented in MEMLET. To optimize thefit by maximum-likelihood, it was necessary to reject outlying datapoints from the distribution (e.g. from the presence of some smallerspecies in solution, or larger aggregates). For unimodal distributions,for example, outliers were defined as those points either with acontrast less than the lower quartile minus 1.5 times the interquartilerange, or greater than the upper quartile plus 1.5 times theinterquartile range.

The dependence on sequence mass of the average value of the contrastdetermined in this way for each of the 8 proteins listed above was fitto a straight line in order to calibrate the system. We used thedifference between the line of best fit and the measured data to assessthe accuracy of the technique, resulting in the average deviation of1.9% from the sequence mass reported in the main text.

Surface Vs Solution Distributions and Corrections

In landing assays, we detect individual molecules binding to the coverglass surface, rather than directly in solution. As a result, variationsin surface affinities and/or collision rates could in principle lead toa deviation of the surface-measured distribution from the true solutiondistribution. We can, however, extract binding rates directly from ourexperimental data. For this, we employ standard flow cells that exhibitlarge surface-to-volume ratios. As a result, binding to the surfacereduces the analyte concentration throughout the experiment, as can beseen in FIG. 10D. We remark that this decay in sticking frequency cannotbe attributed to surface saturation, because (a) we can add more sampleto a flow chamber a few times and still observe binding, (b) for a 4×9μm² field of view, a tightly packed monolayer of 5 nm diameter particleswould contain ˜1.8×10⁶ particles, while we typically measure 10³ bindingevents in an experiment, and (c) the lack of unbinding implies that wedeplete the concentration in solution with time. The measured drop inbinding frequency is well described by an exponential decay, consistentwith a simple first-order process of protein molecules in solutionbinding to the glass surface with a given rate constant (FIG. 17A),which provides representative decay constants from multiple movies (FIG.17B). Because we have sufficient mass resolution to distinguishdifferent oligomeric states, we can characterise the decay in bindingrate for each oligomeric state, measuring a rate constant which is afunction of the surface sticking probability and the collision rate,which in turn is proportional to the diffusion coefficient.

The values of these rate constants for different oligomers and differentspecies studied in this manuscript exhibit variations of less than ±25%from the mean decay for all oligomeric species per protein. For BSA,Env, smooth-muscle myosin and GroEL we observe a decrease in decay ratewith molecular weight. For systems where we observe decay of a native,globular oligomeric structure into smaller subunits (ADH, β-amylase),the pattern is inverted. Importantly, the decay rates are highlyreproducible with narrow standard deviations, demonstrating that theycan be characterised with high accuracy on an oligomer-to-oligomerbasis. We found excellent correlation between the absolute rate constantand the (molecular weight)^(−1/3), i.e. the scaling of the diffusioncoefficient with mass assuming spherical objects, suggesting that thesurface affinity for different oligomeric states, and indeed differentproteins, does not vary significantly (FIG. 17D).

To probe the effect of diffusion and surface attachment on oligomericdistributions and resulting thermodynamics parameters, we can apply acorrection to the counted numbers of each oligomer depending on themeasured binding rate constant. Assuming that any change in theequilibrium distribution as a result of this dilution is slow comparedto the timescale of the experiment, then to accurately count theproportion of each oligomer present in solution, one would have tointegrate over the exponential decay in binding events from the additionof sample (time, t=0) until all binding has ceased. Experimentally,meanwhile, we effectively integrate from some time t₀≈15 s afteraddition of sample up to a later time t_(f) when the acquired movieends. We can relate these two via

$N_{i} = {M_{i}\frac{e^{k_{i}t_{0}}}{1 - e^{- {k_{i}{({t_{f} - t_{0}})}}}}}$

where N_(i) is the number of particles of oligomer i counted over thefull integral, M_(i) is the number measured experimentally, and k_(i) isthe binding rate constant for oligomer i.

As shown for BSA (data not shown) corrections produce noticeable, butnevertheless small corrections to the mass distributions. While onemight assume that the correction should lead to an increase in the dimerfraction because of compensation for diffusion, we find the opposite tobe true. This is caused by the fact that we have to take into accountthe delay (t₀) between addition of the sample and observation of bindingevents, which is usually on the order of 15 s. During this time, ahigher proportion of the smaller oligomers are lost to the surface,which leads to the measured correction factor. We observe similareffects for our Env-BanLec experiments (FIG. 17F), where the correctioncauses changes in the mole fractions that are within the errorassociated with our ability to model the experimental data.

We conclude that non-specific binding to microscope cover glass onlyweakly influences the oligomeric distributions determined byinterferometric light scattering (e.g. iSCAMS), suggesting that ourmeasurements are representative of solution distributions, especiallygiven the fact that they can be corrected by quantifying the probabilityof surface attachment and diffusion coefficient as a function ofoligomeric state.

Accuracy, Noise Floor, Resolution and Precision

The mass deviation between sequence mass and measured mass according toa linear correlation from calibration proteins was <5 kDa. Comparisonwith molecular shape factor as extracted from structural data exhibitedno clear correlation in magnitude or in sign. Therefore, at this stagewe cannot quantitatively connect accuracy, mass and refractivity beyonda general rule that the accuracy is limited to a few kDa, which causesthe percentage deviation to increase for smaller object mass as shown inthe upper panel of FIG. 1A.

The noise floor of our approach, as defined by the standard deviation ofbackground images recorded in the absence of biomolecules, decreases asexpected for a shot noise-limited process for image averaging up toseveral tens of ms, after which it begins to deviate, with a minimumnear 300 ms (FIG. 19B). We believe that the deviation is largely causedby sample drift, causing the surface roughness to begin to contribute tothe ratiometric images. The noise floor represents the instrumentallimit to mass resolution manifested in the width of the recorded massdistributions. In addition, we found that this width increases with mass(FIG. 19C), an effect that may be expected in the presence of anadditional uncertainty that scales with size of the object measured. Wecurrently believe that the source of this additional broadening islargely caused by the rough glass surface, which exhibits±40%peak-to-peak variations in reflectivity in our experimental arrangement.

The theoretical precision, at least in the context of unimodal massdistributions is given by σ/√{square root over (N)}, where σ is thestandard deviation of the distribution and N the number of eventsmeasured. We find that this relationship frequently holds only forN<100, leading to a precision on the order of 2% of the object mass (seeFIG. 11D). As above, we believe that the most likely limiting factorsare the glass roughness and our ability to precisely determine the focusposition from experiment to experiment in a repeatable fashion, inducingcontrast and thus mass variations beyond the theoretical expectation.

Lipid Nanodiscs Preparation and Procedure

Membrane scaffold proteins were expressed in E. coli, purified andassembled by addition of lipids in the molar ratios specified in TableS2, followed by purification by size exclusion chromatography asdescribed previously. The nanodiscs were diluted to 10 nM in 20 mM Tris,100 mM NaCl, pH 7.4, and nonspecific binding to a glass surface wasmeasured according to the procedure described above. For comparison withexpected masses for each sample, we took literature values (Table S3)for the mass of the MSP1D1 nanodisc with DMPC(1,2-dimyristoyl-sn-glycero-3-phosphocholine), which we took as areference, as measured by a variety of techniques to provide a range ofexpected masses. For each of these, including our own, we calculated anexpected mass for the MSP1ΔH5 nanodisc with DMPC. This was done bysubtracting the mass of the protein component and scaling the resultingmass of lipid by the reduction in area of the bilayer patch calculatedfrom the square of the reduction in diameter of the nanodisc as measuredby size exclusion chromatography (SEC), dynamic light scattering (DLS)and electron microscopy (EM). The expected mass of the nanodisc followsby addition of the MSP1ΔH5 protein mass (Tables S3 and S4).

Similarly, for the MSP1D1 nanodiscs with varying lipid composition, wecalculated a range of expected masses from the various reportedmeasurements of the reference nanodisc. We scaled the measured lipidmass according to the expected changes due to different total number oflipid molecules per nanodisc (from the protein:lipid assembly ratio) anddifferent average mass per lipid molecule. Again, addition of the massof protein leads to the expected masses of the nanodiscs (Tables S5 andS6). The total number of detected particles were 14216 (MSP1D1-DMPC),3041 (MSP1D1-DMPC/PC14:1/Chol), 2292 (MSP1D1-PC14:1/Chol), 2277(MSP1Δ1-DMPC) from 2-12 experiments.

Env and BanLec Preparation and Procedure

Env SOSIP (BG505) and BanLec were prepared as described previously,either in the presence or absence of kifunensine. The proteins were eachdiluted to 5 nM in phosphate buffered saline (PBS) and binding to aglass surface was imaged in a flow chamber as described for the landingassay. The total number of detected particles were 15391 and 8048 forkifunensine-treated and wildtype Env, respectively.

For the interaction studies between Env and BanLec, Env was diluted to20 nM in PBS. BanLec was diluted to 2-fold the working concentration inPBS. The protein dilutions were kept on ice until use. Env was mixed 1:1with either PBS (as a control) or BanLec, and incubated for 5 min atroom temperature. Next, 20 μl of the mixture were flushed into aPBS-filled flow chamber, and landing on the glass surface immediatelyrecorded. The kernel density estimates of the probability densitiesshown in FIG. 12A were generated using a Gaussian kernel with bandwidthof 30 kDa. The number of each observed Env cluster was determined bycounting landing events within resolvable contrast intervals, i.e.monomers, dimers, trimers, tetramers and above. In order to determinehow many individual Env molecules were present in each population, thenumber of landing events was multiplied with the respective number ofEnv units per oligomer (1 for monomeric Env, 2 for dimeric Env, 3 fortrimeric Env, etc.). In this way, the fraction of Env molecules inclusters and relative abundance of the different species (FIG. 12B)could be calculated.

In order of increasing BanLec concentration, the total number ofdetected particles were: 4446, 4841, 3068, 3106, 6258, 3893, 7370, 4198,3412, 3027, 3674, 4287, 3790.

Modelling of Env-BanLec Interaction

We modeled the Env (A) BanLec (B) system as

A+A

A ₂ K _(d)

A+B

AB K _(BanLec)

A _(n) B _(n) +A

A _(n+1) B _(n) K _(Env)

A _(n+1) B _(n) +B

A _(n+1) B _(n+1) K′ _(BanLec)

From these equilibria, the concentration of each oligomer can beexpressed in terms of a combination of equilibrium constants and powersof [A] and [B]. Using the fact that the total number of each monomer isconserved, i.e.

$\lbrack A\rbrack_{0} = {{\lbrack A\rbrack + {2\lbrack A_{2} \rbrack} + \lbrack{AB}\rbrack + {\sum\limits_{i = m}^{\infty}{\sum\limits_{n = {m - 1}}^{m}{{m\lbrack {A_{m}B_{n}} \rbrack}\lbrack B\rbrack}_{0}}}} = {\lbrack B\rbrack + \lbrack{AB}\rbrack + {\sum\limits_{i = m}^{\infty}{\sum\limits_{n = {m - 1}}^{m}{n\lbrack {A_{m}B_{n}} \rbrack}}}}}$

where [A]₀ and [B]₀ are the initial concentrations, we thus obtained twosimultaneous equations to solve (numerically) for [A] and [B] in termsof the equilibrium constants and the initial concentrations. Once [A]and [B] are known, the concentrations of all other species follow fromthe equilibrium conditions.

We obtained very good agreement with experiment for K_(Env)≅8,K_(BanLec)≅0.4, K′_(BanLec)≅0.12, and K_(d)≅0.004 all in units of(nM)⁻¹. The initial concentration of Env in the calculation was taken tobe 10 nM.

Supported Livid Bilayer (SLB) Preparation

CultureWell silicone gaskets (Grace Bio-Labs) were cut and placed onto afreshly cleaned coverslip providing four independent 30-50 μl samplechambers on the same substrate. Stock solutions of1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPS) in chloroform werepurchased from Avanti Polar Lipids (Alabaster, Ala.). The DOPC:DOPS(3:1) mixture was dried to a film, kept under vacuum for at least 1 hand brought to a concentration of 1 mg ml⁻¹ in bilayer buffer (10 mMHEPES, pH 6.8, 200 mM NaCl, 2 mM CaCl₂). Using a mini-extruder (AvantiPolar Lipids), the suspension was then forced 21 times through a singlemembrane with a pore size of 100 nm. SLBs were formed by vesicle fusioninside the chamber on cleaned coverslips that have been passed through ablue flame. After 5-10 min incubation, excess vesicles were removed fromthe chamber by rinsing with 10 mM sodium phosphate buffer, pH 7.0.

α-Synuclein Preparation

The construct was expressed and purified as described previously,lyophilized and stored at −20° C. The lyophilized protein was dissolvedat a final concentration of 1-1.5 mM in 20 mM sodium phosphate, pH 7.0.In order to allow complete dissolution of the protein powder, the pH ofthe α-synuclein solution was adjusted to 7.0 with 1 M sodium hydroxide.

α-Synuclein Aggregation Imaging and Analysis

Before addition to the SLBs the protein was diluted in sodium phosphatebuffer. 10 μl of α-synuclein solution added to a sample volume of 30 μl,yielding the final reported concentrations. Addition of α-synuclein wasrecorded at 100 frames/s and aggregate formation was followed for 20 s.The acquired images consisted of 512×512 pixels² with a pixel size of23.4 nm/pixel, resulting in a field of view of 12×12 μm². As with theprotein binding assays, these images were 3×3 pixel-binned beforeadditional processing, giving a final pixel size of 70.2 nm/pixel. Tocapture the initial growth, the entire image stack was divided by abackground image. The background was chosen as the average of framesbefore addition of α-synuclein. After selecting an 11×11 pixel² regionof interest centred on each growing aggregate, we fitted this region ineach frame to the difference-of-two-Gaussians model function, asdescribed for the analysis of protein landing events. The contrast wasplotted as a function of time, and the initial growth rate wasdetermined by a linear fit for 0.1-1 s of data after addition ofα-synuclein, depending on concentration.

Preparation of Biotin-PEG Flow Chambers for Actin Polymerization Assays

Microscope cover glass (No. 1.5, 24×50 mm₂, VWR) was sonicatedsequentially in 2% Hellmanex, H₂O and ethanol each for 10 min, then 0.1M KOH for 15 min and finally 5 min in H₂O. In between each step, theywere washed with H₂O to remove excess solution from the previous step.All coverslips were individually rinsed with H₂O and ethanol, thenblow-dried with a clean stream of nitrogen. A solution of 2 mg ml-1mPEG-silane (MW 2000, LaysanBio) and 0.1 mg ml⁻¹ biotin-PEG-silane (MW3400, LaysanBio) in 80% ethanol at pH 2.0 (adjusted with HCl) wasprepared immediately before being sandwiched between two cleanedcoverslips. The sandwiches were incubated in petri-dishes at 70° C. for16 h. The biotinylated coverslips were vigorously rinsed with H₂O andethanol in an alternating fashion removing any dried excess PEG, thenblow-dried with a clean stream of nitrogen. Small coverslips (No. 1.5,24×24 mm², VWR) were rinsed with H₂O and ethanol in an alternatingfashion and blow-dried with nitrogen. Flow chambers were assembled asdescribed above. The flow chambers were stored in a dry nitrogenatmosphere at −20° C.

Actin In Vitro Polymerization

Rabbit skeletal muscle actin was purified as described previously.Biotinylated actin was purchased from Cytoskeleton Inc. (Denver, USA,Cat. no. AB07-A). Avidin (Cat. no. A9275) was purchased from SigmaAldrich. A biotin-PEG flow chamber was filled with G-actin buffer (2 mMTris-HCl, 0.2 mM CaCl₂), pH 8.0), flushed with 40 μl of 10 μg ml⁻¹avidin in G-actin buffer and incubated for 5 min. Excess avidin wasflushed out with 40 μl of G-actin buffer. Immediately before addition tothe flow chamber, polymerization of a mixture of G-actin and 1%biotinylated G-actin (final concentrations: 300-1000 nM actin, 3-10 nMbiotin-actin) in G-actin buffer containing 0.2 mM ATP and 2 mM DTT wasinduced by adding 1/10 of a volume of 1×KMEH buffer (1×concentration: 10mM HEPES, 50 mM KCl, 2 mM MgCl₂, 1 mM EGTA, pH 7.4). A volume of 50 μlof the polymerization mixture was flowed into the chamber. A 9×9 μm²field of view was recorded at 468 frames/s.

Actin Polymerization Data Analysis Procedure

Actin polymerization were analyzed using custom software written inLabVIEW. The raw video was 2×2 pixel-binned, resulting in an effectivepixel size of 46.8 nm/pixel. To visualize the actin filaments on top ofthe signal from the glass surface roughness, a background image wascreated by taking a median of 20 raw images, and used tobackground-correct subsequent frames. To reduce shot noise, 8consecutive frames were averaged, which gave an effective frame rate of58.5 Hz. Actin filament tips were tracked by selecting a region ofinterest that included only the tip to be analyzed. Each frame of thisregion was then fit using a filament tip model function, consisting of aGaussian wall w(x, y) starting at x₀ and y₀ running in direction θ withwidth σ:

${w( {x,y} )} = {\exp \lbrack {- \frac{( {{( {x - x_{0}} )\sin \; \theta} + {( {y - y_{\theta}} )\cos \; \theta}} )^{2}}{2\; \sigma^{2}}} \rbrack}$

which is attached to half of a symmetric 2D Gaussian g(x, y) having itscentre at x₀ and y₀ with width σ

${g( {x,y} )} = {\exp\lbrack {- \frac{( {x - x_{0}} )^{2} + ( {y - y_{0}} )^{2}}{2\; \sigma^{2}}} \rbrack}$

The two functions are attached to each other by defining the border b(x,y) between them in the following manner:

${f( {x,y} )} = {{( {y - y_{0}} ){\cos ( {\theta - \frac{\pi}{2}} )}} - {( {x - x_{0}} ){\sin ( {\theta - \frac{\pi}{2}} )}} + 0.5}$${b( {x,y} )} = \{ {\begin{matrix}{0,{{f( {x,y} )} < 0}} \\{{f( {x,y} )},{0 \leq {f( {x,y} )}}} \\{1,{{f( {x,y} )} > 1}}\end{matrix} \leq 1} $

The tip function t(x,y) with an amplitude A is then created as:

t(x,y)=A[b(x,y)g(x,y)+(1−b(x,y))w(x,y)]

A LabVIEW representation of this function was fitted to the filament tipimages using the Levenberg-Marquardt algorithm. The filament tipposition was defined by the best fit values for x₀ and y₀. Thetrajectories were rotated such that the growth axis was aligned with thex-axis of the coordinate system. Step traces as shown in FIG. 13D werecreated by plotting the x-axis position vs. time.

Steps were automatically detected by a LabVIEW implementation of apreviously described step finding algorithm. Briefly, we describe thestep traces as a sequence of values, x₁, x₂, . . . , x_(n), which aredrawn from an unknown number of normal distributions of equal variance(σ²) but different means (μ). The algorithm then searches for the changepoints of μ, i.e. step positions, in the data series, one at a time.This is done by segmenting the sequence at each position k=1, . . . ,n−1 and testing the null hypothesis

H ₀:μ₁=μ₂= . . . =μ_(n)

against the alternative

H ₁: μ₁= . . . =μ_(k) ₀ ≠μ_(k) ₀ ₊₁= . . . μ_(n)

where 1<k₀<n is the unknown position of a change point. Once a changepoint is found the sequence is divided into two sequences before andafter the accepted change point. For each sequence the process isrepeated until no more change points are found, i.e. the null hypothesisis accepted. Hypothesis testing was performed based on the principle ofminimization of the Schwarz information criterion (SIC), defined by

SIC=p log n−2 log L(θ)

where L(θ) is the maximum likelihood function for the model, p is thenumber of free parameters in the model, and n is the sample size. WithSIC(n) being the SIC under H₀, and SIC(k) being the SIC under H₁, for achange point at a position k=2, . . . , n−2. The hypothesis H₀ isaccepted if SIC(n)≤min_(k) SIC(k), otherwise H₀ is rejected if there isa k for which SIC(n)>SIC(k). The change point position is chosen to bewhere SIC(k) is smallest in the data sequence. For our case, assuming ashifting mean and a constant variance, the two SIC values are obtainedas

SIC(n) = n log  2 π + n log  σ² + n + 2log  nSIC(k) = n log  2 π + k log  σ₁² + (n − k)log  σ_(n)² + n + 3 log  nwhere${\sigma^{2} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}( {x_{i} - \overset{\_}{x}} )^{2}}}},{\sigma_{1}^{2} = {\frac{1}{k}{\sum\limits_{i = 1}^{k}( {x_{i} - \overset{\_}{x}} )^{2}}}},{and}$$\sigma_{n}^{2} = {\frac{1}{n - k}{\sum\limits_{i = {k + 1}}^{n}{( {x_{i} - \overset{\_}{x}} )^{2}.}}}$

Since this method works solely based on comparison of the SIC values, itdoes not require any input other than the data sequence and finds stepswithout user bias. We only included change points that are at leastthree data points apart from another, in order to eliminate steps thatare found based on large fluctuations on a very short timescale. Weemphasize that changing the minimum delay had a negligible effect on theobtained step sizes (FIG. 23E,F). The distribution of step sizes wasdescribed by a Gaussian mixture model using the expectation maximizationalgorithm in MATLAB. Errors of the fitting parameters were estimatedusing a bootstrap procedure with 1000 bootstrap samples.

Phalloidin-Actin Control Experiment

We attempted a control experiment with static actin filaments to providea baseline in terms of filament end tracking and to determine whetherany end displacements could result from the entire filament movingacross the surface. Unfortunately for this experiment, actin filamentsare generally dynamic at their ends and the absence of G-actin insolution causes their depolymerisation. G-actin at the criticalconcentration causes no net growth, but subunits will exchange at thefilament tips. To minimize this effect, we used the actin filamentstabilizer phalloidin. A mixture of 10 μM G-actin and 0.1 μMbiotin-G-actin in G-actin buffer containing 0.2 mM ATP and 2 mM DTT waspolymerized for 1 h at room temperature by adding 1/10 of the volume of10×KMEH. It was then diluted 5-fold in 1×KMEH and mixed with 3 μMphalloidin (Sigma P2141) in 1×KMEH. The filaments and phalloidin wereincubated overnight at 4° C. On the day of the experiment, thephalloidin-stabilized filaments were diluted to 100 nM in 1×KMEH andkept on ice until use. The biotin-avidin-flow chambers were prepared asdescribed above, then 30 μl phalloidin-actin were added. The attachmentof filaments was monitored under similar imaging conditions as thepolymerization experiments.

The presence of phalloidin markedly reduced any dynamics at the filamenttips, but did not fully stop them at our levels of sensitivity. Wecould, however, correlate the displacements of the two ends ofindividual filaments in order to rule out that any tip positionfluctuations are caused by an overall movement of the filaments. Weselected 9 short phalloidin-stabilized filaments whose two tips wereboth visible and isolated from other filaments (FIG. 23A). We trackedboth tips and determined their displacements along the filament axis asdescribed above, orienting the resulting trajectories such that the twotips were facing opposite directions. We found no correlation betweenthe displacements of the two tips on the same filament (FIG. 23B)suggesting that length fluctuations at the two filament ends are notcaused by movement of the filament.

Actin Filament Growth Simulation

To test the fidelity of our step finding algorithm, custom writtensoftware in LabVIEW created movies of growing actin tips. The modelfunction described in the actin polymerization data analysis proceduresection was used to generate actin filament tips with amplitude A=−0.06and width σ==1.7 pixels in a 15×15 pixel² image, which is similar to theexperimental values assuming a pixel size of 46.8 nm. The experimentalshot noise level was determined by dividing the respective pixel valuesof one frame in the experimental videos (averaged to 58.5 Hz) by thoseof the previous frame and determining the standard deviation of thepixel values in the resulting differential images. The LabVIEW Gaussiannoise generator was used to produce images with the experimental shotnoise level (1.8×10⁻³), which was then added to the filament tip images.The length of the actin filament tip was extended or shortened inconsecutive frames by moving the tip position a defined distance (here1, 2, 2.7, 4, 5 or 8 nm) forward or backward along the filament axis.

The dwell times of the tip between forward steps or backward steps wererandomly chosen from two pools of exponentially distributed dwell timesgenerated according to T=−lnU/k, where T is the dwell time, U auniformly distributed random number between 0 and 1, and k the rateconstant. The rate constant for subunit attachment (forward step) k₁ was4.887 s⁻¹ and the rate for subunit detachment (backward step) k⁻¹ was2.103 s⁻¹, both based on experimentally determined kinetics (FIG. 23C at0.3 μM). The dwell times were used to create a sequence of attachmentand detachment events, which were treated independently, for a timeperiod of at least the length of the simulated video. For a simulatedvideo of 15 s at 58.5 Hz frame rate the program checks each framewhether an event is due according to the sequence of events generatedbefore and either executes the event or leaves the filament unchanged.Multiple events happening during one frame time is possible andaccounted for. The simulated image sequences are analyzed in the sameway as the experimental videos as described in the actin polymerizationdata analysis procedure section. The simulation with 2.7 nm step sizematches our step size histogram in FIG. 13E well, with smaller stepsdetectable, albeit returning a larger than defined step size (FIG. 23D).Overall, these simulations demonstrate that the assumption-freestep-detection algorithm is capable of robustly identifying andquantifying 2.7 nm given the experimental noise level.

Determination of Actin Macroscopic Growth Rates

A smoothing spline was fitted to the 2D trajectories obtained fromtracking actin filament tips, as in the actin polymerization dataanalysis procedure section. The length of this spline was used as theaverage tip displacement d. The average elongation velocity v of thefilament tip was calculated according to

$v = \frac{d \times F_{r}}{n}$

where F_(r) is the frame rate and n the number of frames, leading to theaverage elongation rate

$r = \frac{v}{A_{s}}$

where A_(s) is the actin subunit size, assumed to be 2.7 nm. Thisprocedure was repeated for a number of actin filaments growing atdifferent actin concentrations. The elongation rate as a function ofactin concentration can be described by

r=k ₊₁[actin]−k ⁻¹

where k₊₁ is the subunit association rate constant, k⁻¹ is the subunitdissociation constant and K_(crit)=k⁻¹/k₊₁ is the critical concentrationfor actin polymerization. The critical concentration (129±151 nM) andrate constants (k₊₁=16.3±5.0 μM⁻¹ s⁻¹ and k⁻¹=2.1±2.4 s⁻¹) obtained fromFIG. S23C agrees well with previous studies.

TABLE S1 Contributions to R, V_(i), and M_(i) for the canonical aminoacids. Amino acid n_(aa)/g V_(i)/(cm³/mol) M_(i)/Da A 0.242 54.26723 89.0932 R 0.253 116.12344 174.201  N 0.229 76.79325 132.1179 D 0.22770.52933 133.1027 C 0.238 62.33805 121.1582 Q 0.237 89.98362 146.1445 E0.233 84.80384 147.1293 G 0.225 38.42674  75.0666 H 0.253 95.94639155.1546 I 0.282 99.31927 131.1729 L 0.279 99.13858 131.1729 K 0.266102.391 146.1876 M 0.263 101.00571 149.2113 F 0.287 116.54505 165.1891 P0.245 74.14313 115.1305 S 0.22  56.73666 105.0926 T 0.236 72.276119.1192 W 0.297 139.55291 204.2252 Y 0.272 118.71333 181.1885 V 0.27 83.77993 117.1463

TABLE S2 Composition key (all with MSP1D1 as the scaffold protein).Protein: Relative Com- Average lipid mass of po- Mass/ Lipid assemblylipid sition Lipid Percentage Da Mass/Da ratio content a* DMPC 100 677.9677.9 1:80   1 b DMPC 40 646.8 646.8 1:73.5 0.877 PC14:1 50 645.2Cholesterol 10 386.7 c PC14:1 90 673.9 645.2 1:67   0.797 Cholesterol 10386.7 *MSP1D1/DMPC nanodisc taken as reference for size comparison.

TABLE S3 Reduction in total mass of nanodisc calculated for a range ofmeasured masses of the MSP1D1 DMPC nanodisc. All values are given inkDa. SEC, DLS, NMR, and native MS data are from the literature. MSP1D1Exp. MSP1ΔH5 Exp. MSP1ΔH5 MSP1D1 lipid lipid mass nanodisc mass massTechnique mass SEC DLS EM SEC DLS EM 141.0 iSCAMS  93.9 74.6 72.9 67.4116.6 114.9 109.4 124.0 SEC  76.9 61.1 59.7 55.2 103.1 101.7  97.2 126.0DLS  78.9 62.7 61.2 56.6 104.7 103.2  98.6 149.5 Native 102.4 81.4 79.573.5 123.3 121.5 115.5 MS 158.0 NMR 110.9 88.1 86.1 79.6 130.1 128.1121.6

TABLE S4 Size comparison for lipid nanodiscs. Reduction in area of lipidbilayer patch in nanodisc with reduction in size of scaffold protein,calculated from reduction in hydrodynamic diameter as measured by SEC,DLS or EM. D = diameter, R_(lo) = radius of lipid-only content assumingthe belt protein contributes 0.5 nm to the radius. Ratio of lipid areas:MSP1D1 MSP1ΔH5 MSP1ΔH5/ Technique D/nm R_(lo)/mm D/nm R_(lo)/mm MSP1D1SEC 10.2  4.6 9.2 4.1 0.79 DLS 9.4 4.2 8.4 3.7 0.78 EM 9.5  4.25 8.2 3.60.72

TABLE S5 Composition b predictions. All values are given in kDa. Lipidmass Expected Mass of in MSP1D1 Expected nanodisc MSP1D1 nanodisc lipidmass mass 141.0 93.9 82.3 129.4 124.0 76.9 67.4 114.5 126.0 78.9 69.2116.3 149.5 102.4  89.8 136.9 158.0 110.9  97.2 144.3

TABLE S6 Composition c predictions. All values are given in kDa. Lipidmass Expected Mass of in MSP1D1 Expected nanodisc MSP1D1 nanodisc lipidmass mass 141.0 93.9 74.9 121.9 124.0 76.9 61.3 108.4 126.0 78.9 62.9110.0 149.5 102.4  81.6 128.7 158.0 110.9  88.4 135.5

TABLE S7 Contrast-mass conversions (linear fit parameters to a contrastvs mass calibration plot as shown in FIG. 11A) for all data shown inFIG. 11. The different datasets were taken at different times, and as aresult of the use of partial reflectors of different transmissivity,oxidation of the partial reflector, and minor drifts in alignment, thevalues for converting between mass and contrast were different overtime. The setup was calibrated for each measurement using the procedureoutlined for FIG. 11A, and for ease and consistency of display thecontrasts shown for each measurement in FIGS. 11 and 18 were normalizedto the contrast as in FIG. 10. Figures Description Slope/kDa⁻¹ Intercept1, 2B, BSA; streptavidin- 6.5651E−05 4.2324E−04 S5C biotin binding, 2A,S5A Representative 2.0529E−05 3.3099E−04 Calibration 2C, S5D Lipidnanodiscs 1.6483E−05 0.0000E+00 2D, S5E Env +/− kifunensine 1.5410E−050.0000E+00 comparison

TABLE S8 Abbreviations used in FIG. 2. Abbreviation Meaning DescriptionSEC size-exclusion chromato- Experimental techniques graphy used in theliterature for DLS dynamic light scattering mass determination of MS(native) mass spectrometry the MSP1D1/DMPC NMR nuclear magneticresonance nanodisc MSP1D1 Membrane scaffold protein Membrane scaffold(MSP) 1 with the first 11 N- proteins (MSPs) used to terminal aminoacids removed, make the lipid nanodiscs as described in (51) MSP1ΔH5MSP1D1 with the 5th α-helix deleted, as described in (49) DMPC1,2-dimyristoyl-sn-glycero-3- Lipids used in the lipid phosphocholinenanodiscs PC14:1 1,2-dimyristoleoyl-sn-glycero- 3-phosphocholine CholCholesterol DSG3 desmoglein-3 Biotinylated peptides ACTHadrenocorticotropic hormone in FIG. 2D*

1. A method of quantifying the mass of an object, wherein the mass ofsaid object is quantified by interferometric light scattering, andwherein said mass is quantified with up to 5% mass error.
 2. A methodaccording to claim 1, wherein said mass is quantified with equal or lessthan 2% mass error.
 3. A method according to claim 1, wherein said massis quantified within lkDa of the sequence mass of the object.
 4. Amethod according to claim 1, wherein said object is 19 kDa or greater insize.
 5. A method according to claim 1, wherein said object is a weakscatterer of light.
 6. A method according to claim 1, wherein saidobject is a nucleic acid molecule.
 7. A method according to claim 1wherein said object is a virus-like particle.
 8. A method according toclaim 1, wherein said object is a single protein.
 9. A method accordingto claim 1, wherein said object is a glycoprotein.
 10. A methodaccording to claim 1, wherein said object is in solution.
 11. A methodof measuring or quantifying a change in the mass of an object, whereinthe change in mass of said object is measured or quantified byinterferometric light scattering.
 12. A method of claim 11, wherein themass of the object changes due to one or more events selected from thegroup consisting of single molecule binding/unbinding, phase transition,clustering, assembly/disassembly, aggregation, one or moreprotein/protein interactions and/or one or more protein/small moleculeinteractions.
 13. A method of claim 11, wherein the mass of the objectchanges due to oligomeric assembly or glycoprotein cross-linking. 14.The method of claim 11, wherein the change in mass of the object istime-resolved, optionally at a specific position and/or localconcentration of said object.
 15. The method of claim 11 wherein thechange of mass of the object is measured in a position and localconcentration sensitive manner.
 16. The method of claim 11, wherein oneor more interactions resulting in change in the mass of the object arequantified.
 17. The method of claim 11, further comprising determiningthermodynamic and/or kinetic parameters influencing the change in themass of the object or of one or more interactions resulting in change inthe mass of the object.
 18. The method of claim 11, wherein the mass ofsaid object is from 10 kDa to 5000 KDa.
 19. The method of claim 11wherein the change of mass of the object is measured over time in alocalisation-dependent manner, optionally wherein the localisation isprecise at the level of sub-diffraction.
 20. The method of claim 11wherein the change of mass of the object is due to binding anddissociation of binding partners.
 21. The method of claim 11 wherein thechange of mass of the object allows the correlation of binding events tosingle molecule fluorescence localization measurements, preferably toidentify specific binding partners labelled with fluorescent molecules.22. The method of claim 11 wherein the change of mass of the object isdue to an interaction with a binding partner of known specificity todetermine the identity of the object.
 23. An interferometric scatteringmicroscope comprising: a sample holder for holding a sample in a samplelocation; an illumination source arranged to provide illuminating light;a detector; an optical system being arranged to direct illuminatinglight onto the sample location and being arranged to collect outputlight in reflection, the output light comprising both light scatteredfrom the sample location and illuminating light reflected from thesample location, and to direct the output light to the detector; and aspatial filter positioned to filter the output light, the spatial filterbeing arranged to pass output light but with a reduction in intensitythat is greater within a predetermined numerical aperture than at largernumerical apertures.
 24. An interferometric scattering microscopeaccording to claim 23, wherein the predetermined numerical aperture isthe numerical aperture of the illuminating light reflected from thesample location that is comprised in the output light.
 25. Aninterferometric scattering microscope according to claim 23, wherein thespatial filter is arranged to pass output light with a reduction inintensity within said predetermined numerical aperture to 10⁻² of theincident intensity or less.
 26. An interferometric scattering microscopeaccording to claim 23, wherein the spatial filter is arranged to passoutput light with a reduction in intensity within said predeterminednumerical aperture to 10⁻⁴ of the incident intensity or more.
 27. Aninterferometric scattering microscope according to claim 23, wherein thepredetermined numerical aperture is less than 1, preferably less than0.5.
 28. An interferometric scattering microscope according to claim 23,wherein the illuminating light is spatially and temporally coherent. 29.An interferometric scattering microscope according to claim 23, whereinthe optical system comprises a beam splitter arranged to split theoptical paths for the illuminating light and the output light, thespatial filter being part of the beam splitter.
 30. An interferometricscattering microscope according to claim 23, wherein the spatial filteris transmissive.
 31. An interferometric scattering microscope accordingto claim 23, wherein the spatial filter is reflective.
 32. Aninterferometric scattering microscope according to any of the precedingclaims wherein the sample holder incorporates a solid immersion lens.33. An interferometric scattering microscope according to claim 32,wherein the solid immersion lens is hemispherical or superhemispherical.34. An interferometric scattering microscope according to claim 23,wherein the optical system includes an objective lens and the spatialfilter is positioned directly behind the back aperture of the objectivelens.
 35. An interferometric scattering microscope according to claim23, wherein the optical system includes an objective lens and thespatial filter is positioned at a conjugate focal plane of the backfocal plane of the objective lens.
 36. An interferometric scatteringmicroscope according to claim 23, wherein the sample holder holds asample comprising objects having a mass of 5000 kDa or less.
 37. Aninterferometric scattering microscope according to claim 36, wherein thesample holder holds a sample comprising objects having a mass of 10 kDaor more.
 38. An interferometric scattering microscope according to claim23, wherein the sample holder holds a sample comprising objects having ascattering cross section with respect to the illuminating light of 10⁻¹m² or less.
 39. An interferometric scattering microscope according toclaim 36, wherein the sample holder holds a sample comprising objectshaving a scattering cross section with respect to the illuminating lightof 10⁻²⁶ m² or more.
 40. An interferometric scattering microscopeaccording to claim 23, wherein the microscope is arranged to operate ina wide-field mode and detector comprises an image sensor that isarranged to capture an image of the sample.
 41. An interferometricscattering microscope according to claim 23, wherein the microscope isarranged to operate in a confocal mode, and the microscope furthercomprises a scanning arrangement arranged to scan a region of the sampleto build up an image.
 42. An interferometric scattering microscopeaccording to claim 23, wherein the sample holder comprises a surface forholding the sample thereon.
 43. The method according to claim 1, 11 or19 to 22, comprising use of an interferometric scattering microscope asdefined in claim
 23. 44. A method according to claim 43, wherein themicroscope is arranged to operate in a wide-field mode and detectorcomprises an image sensor that is arranged to capture an image of thesample.
 45. A method according to claim 43, wherein the microscope isarranged to operate in a confocal mode, and the microscope furthercomprises a scanning arrangement arranged to scan a region of the sampleto build up an image.
 46. A method according to claim 43, wherein thesample holder comprises a surface for holding the sample thereon.
 47. Amethod of adapting an interferometric scattering microscope, the methodcomprising providing a spatial filter that performs spatial filtering ofoutput light in reflection, which output light comprises both lightscattered from a sample at a sample location and illuminating lightreflected from the sample location, prior to detection of the outputlight, the spatial filtering passing the output light but with anintensity reduction that is greater within a predetermined numericalaperture than at larger numerical apertures, the method furthercomprising detecting the interferometric contrast to detect weaklyscattering objects.
 48. A method according to claim 47, wherein thepredetermined numerical aperture is the numerical aperture of theilluminating light reflected from the sample location that is comprisedin the output light.
 49. A method according to claim 47 or 48, whereinthe spatial filter is arranged to pass output light with a reduction inintensity within said predetermined numerical aperture 10⁻² of theincident intensity or less.
 50. A method according to any one of claims47 to 49, wherein the spatial filter is arranged to pass output lightwith reduction in intensity within said predetermined numerical apertureto 10⁻⁴ of the incident intensity or more.
 51. A method according to anyone of claims 49 to 50, wherein the predetermined numerical aperture isless than 1, preferably less than 0.5.
 52. A method according to any oneof claims 47 to 51, wherein the illuminating light is spatially andtemporally coherent.
 53. A method according to any one of claims 47 to52, wherein the sample holder holds a sample comprising objects having amass of 5000 kDa or less.
 54. An interferometric scattering microscopeaccording to claim 53, wherein the sample holder holds a samplecomprising objects having a mass of 10 kDa or more.
 55. A methodaccording to any one of claims 47 to 54, wherein the sample comprisesobjects having a scattering cross section with respect to theilluminating light of 10⁻¹² m² or less.
 56. A method according to claim55, wherein the sample comprises objects having a scattering crosssection with respect to the illuminating light of 10⁻²⁰ m² or more. 57.A method according to any one of claims 47 to 56, wherein the microscopeis arranged to operate in a wide-field mode and detector comprises animage sensor that is arranged to capture an image of the sample.
 58. Amethod according to any one of claims 47 to 56, wherein the microscopeis arranged to operate in a confocal mode, and the microscope furthercomprises a scanning arrangement arranged to scan a region of the sampleto build up an image.
 59. A method according to any one of claims 47 to58, wherein the sample holder comprises a surface for holding the samplethereon.